//! Advanced Activity Recognition Framework
//!
//! This module provides sophisticated activity_ recognition capabilities including:
//! - Real-time action detection and classification
//! - Complex activity_ sequence analysis
//! - Multi-person interaction recognition
//! - Context-aware activity_ understanding
//! - Temporal activity_ modeling
//! - Hierarchical activity_ decomposition

#![allow(dead_code, missing_docs)]

use crate::error::{Result, VisionError};
use crate::scene_understanding::SceneAnalysisResult;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView3};
use std::collections::HashMap;

/// Advanced-advanced activity_ recognition engine with multi-level analysis
pub struct ActivityRecognitionEngine {
    /// Action detection modules
    action_detectors: Vec<ActionDetector>,
    /// Activity sequence analyzer
    sequence_analyzer: ActivitySequenceAnalyzer,
    /// Multi-person interaction recognizer
    interaction_recognizer: MultiPersonInteractionRecognizer,
    /// Context-aware activity_ classifier
    context_classifier: ContextAwareActivityClassifier,
    /// Temporal activity_ modeler
    temporal_modeler: TemporalActivityModeler,
    /// Hierarchical activity_ decomposer
    hierarchical_decomposer: HierarchicalActivityDecomposer,
    /// Activity knowledge base
    knowledge_base: ActivityKnowledgeBase,
}

/// Action detection with advanced-high precision
#[derive(Debug, Clone)]
pub struct ActionDetector {
    /// Detector name
    name: String,
    /// Supported action types
    action_types: Vec<String>,
    /// Detection confidence threshold
    confidence_threshold: f32,
    /// Temporal window for action detection
    temporal_window: usize,
    /// Feature extraction method
    feature_method: String,
}

/// Activity sequence analysis for understanding complex behaviors
#[derive(Debug, Clone)]
pub struct ActivitySequenceAnalyzer {
    /// Maximum sequence length
    max_sequence_length: usize,
    /// Sequence pattern models
    pattern_models: Vec<SequencePattern>,
    /// Transition probabilities
    transition_models: HashMap<String, TransitionModel>,
    /// Anomaly detection parameters
    anomaly_params: AnomalyDetectionParams,
}

/// Multi-person interaction recognition
#[derive(Debug, Clone)]
pub struct MultiPersonInteractionRecognizer {
    /// Interaction types
    interaction_types: Vec<InteractionType>,
    /// Person tracking parameters
    tracking_params: PersonTrackingParams,
    /// Social distance modeling
    social_distance_model: SocialDistanceModel,
    /// Group activity_ recognition
    group_recognition: GroupActivityRecognition,
}

/// Context-aware activity_ classification
#[derive(Debug, Clone)]
pub struct ContextAwareActivityClassifier {
    /// Context features
    context_features: Vec<ContextFeature>,
    /// Environment classifiers
    environment_classifiers: Vec<EnvironmentClassifier>,
    /// Object-activity_ associations
    object_associations: HashMap<String, Vec<String>>,
    /// Scene-activity_ correlations
    scene_correlations: HashMap<String, ActivityDistribution>,
}

/// Temporal activity_ modeling for understanding dynamics
#[derive(Debug, Clone)]
pub struct TemporalActivityModeler {
    /// Temporal resolution
    temporal_resolution: f32,
    /// Memory length for temporal modeling
    memory_length: usize,
    /// Recurrent neural network parameters
    rnn_params: RNNParameters,
    /// Attention mechanisms
    attention_mechanisms: Vec<TemporalAttention>,
}

/// Hierarchical activity_ decomposition
#[derive(Debug, Clone)]
pub struct HierarchicalActivityDecomposer {
    /// Activity hierarchy levels
    hierarchy_levels: Vec<ActivityLevel>,
    /// Decomposition rules
    decomposition_rules: Vec<DecompositionRule>,
    /// Composition rules for building complex activities
    composition_rules: Vec<CompositionRule>,
}

/// Activity knowledge base for reasoning
#[derive(Debug, Clone)]
pub struct ActivityKnowledgeBase {
    /// Activity definitions
    activity_definitions: HashMap<String, ActivityDefinition>,
    /// Activity ontology
    ontology: ActivityOntology,
    /// Common activity_ patterns
    common_patterns: Vec<ActivityPattern>,
    /// Cultural activity_ variations
    cultural_variations: HashMap<String, Vec<ActivityVariation>>,
}

/// Comprehensive activity_ recognition result
#[derive(Debug, Clone)]
pub struct ActivityRecognitionResult {
    /// Detected activities
    pub activities: Vec<DetectedActivity>,
    /// Activity sequences
    pub sequences: Vec<ActivitySequence>,
    /// Person interactions
    pub interactions: Vec<PersonInteraction>,
    /// Overall scene activity_ summary
    pub scene_summary: ActivitySummary,
    /// Temporal activity_ timeline
    pub timeline: ActivityTimeline,
    /// Confidence scores
    pub confidence_scores: ConfidenceScores,
    /// Uncertainty quantification
    pub uncertainty: ActivityUncertainty,
}

/// Detected activity_ with rich metadata
#[derive(Debug, Clone)]
pub struct DetectedActivity {
    /// Activity class
    pub activity_class: String,
    /// Activity subtype
    pub subtype: Option<String>,
    /// Confidence score
    pub confidence: f32,
    /// Temporal bounds (start, end)
    pub temporal_bounds: (f32, f32),
    /// Spatial region
    pub spatial_region: Option<(f32, f32, f32, f32)>,
    /// Involved persons
    pub involved_persons: Vec<PersonID>,
    /// Involved objects
    pub involved_objects: Vec<ObjectID>,
    /// Activity attributes
    pub attributes: HashMap<String, f32>,
    /// Motion characteristics
    pub motion_characteristics: MotionCharacteristics,
}

/// Activity sequence representing complex behavior chains
#[derive(Debug, Clone)]
pub struct ActivitySequence {
    /// Sequence ID
    pub sequence_id: String,
    /// Component activities
    pub activities: Vec<DetectedActivity>,
    /// Sequence type
    pub sequence_type: String,
    /// Sequence confidence
    pub confidence: f32,
    /// Transition probabilities
    pub transitions: Vec<ActivityTransition>,
    /// Sequence completeness
    pub completeness: f32,
}

/// Person interaction recognition
#[derive(Debug, Clone)]
pub struct PersonInteraction {
    /// Interaction type
    pub interaction_type: String,
    /// Participating persons
    pub participants: Vec<PersonID>,
    /// Interaction strength
    pub strength: f32,
    /// Duration
    pub duration: f32,
    /// Spatial proximity
    pub proximity: f32,
    /// Interaction attributes
    pub attributes: HashMap<String, f32>,
}

/// Overall activity_ summary for the scene
#[derive(Debug, Clone)]
pub struct ActivitySummary {
    /// Dominant activity_
    pub dominant_activity: String,
    /// Activity diversity index
    pub diversity_index: f32,
    /// Energy level of the scene
    pub energy_level: f32,
    /// Social interaction level
    pub social_interaction_level: f32,
    /// Activity complexity score
    pub complexity_score: f32,
    /// Unusual activity_ indicators
    pub anomaly_indicators: Vec<AnomalyIndicator>,
}

/// Temporal activity_ timeline
#[derive(Debug, Clone)]
pub struct ActivityTimeline {
    /// Timeline segments
    pub segments: Vec<TimelineSegment>,
    /// Timeline resolution
    pub resolution: f32,
    /// Activity flow patterns
    pub flow_patterns: Vec<FlowPattern>,
}

/// Confidence scores for different aspects
#[derive(Debug, Clone)]
pub struct ConfidenceScores {
    /// Overall recognition confidence
    pub overall: f32,
    /// Per-activity_ confidences
    pub per_activity: HashMap<String, f32>,
    /// Temporal segmentation confidence
    pub temporal_segmentation: f32,
    /// Spatial localization confidence
    pub spatial_localization: f32,
}

/// Uncertainty quantification for activity_ recognition
#[derive(Debug, Clone)]
pub struct ActivityUncertainty {
    /// Epistemic uncertainty (model uncertainty)
    pub epistemic: f32,
    /// Aleatoric uncertainty (data uncertainty)
    pub aleatoric: f32,
    /// Temporal uncertainty
    pub temporal: f32,
    /// Spatial uncertainty
    pub spatial: f32,
    /// Class confusion matrix
    pub confusion_matrix: Array2<f32>,
}

// Supporting types for activity_ recognition
/// Unique identifier for a person in the scene
pub type PersonID = String;
/// Unique identifier for an object in the scene
pub type ObjectID = String;

/// Motion characteristics of detected activities
#[derive(Debug, Clone)]
pub struct MotionCharacteristics {
    /// Velocity of the motion
    pub velocity: f32,
    /// Acceleration of the motion
    pub acceleration: f32,
    /// Direction of the motion in radians
    pub direction: f32,
    /// Smoothness score of the motion
    pub smoothness: f32,
    /// Periodicity measure of the motion
    pub periodicity: f32,
}

/// Transition between activities
#[derive(Debug, Clone)]
pub struct ActivityTransition {
    /// Source activity_ name
    pub from_activity: String,
    /// Target activity_ name
    pub to_activity: String,
    /// Transition probability
    pub probability: f32,
    /// Typical duration of the transition
    pub typical_duration: f32,
}

/// Indicator of anomalous behavior
#[derive(Debug, Clone)]
pub struct AnomalyIndicator {
    /// Type of anomaly detected
    pub anomaly_type: String,
    /// Severity level of the anomaly
    pub severity: f32,
    /// Description of the anomaly
    pub description: String,
    /// Temporal location of the anomaly
    pub temporal_location: f32,
}

/// Timeline segment representing a period of activity_
#[derive(Debug, Clone)]
pub struct TimelineSegment {
    /// Start time of the segment
    pub start_time: f32,
    /// End time of the segment
    pub end_time: f32,
    /// Dominant activity_ in this segment
    pub dominant_activity: String,
    /// Mix of activities and their proportions
    pub activity_mix: HashMap<String, f32>,
}

/// Flow pattern in activity_ analysis
#[derive(Debug, Clone)]
pub struct FlowPattern {
    /// Type of flow pattern
    pub pattern_type: String,
    /// Frequency of the pattern
    pub frequency: f32,
    /// Amplitude of the pattern
    pub amplitude: f32,
    /// Phase offset of the pattern
    pub phase: f32,
}

#[derive(Debug, Clone)]
pub struct SequencePattern {
    pub pattern_name: String,
    pub activity_sequence: Vec<String>,
    pub temporal_constraints: Vec<TemporalConstraint>,
    pub occurrence_probability: f32,
}

#[derive(Debug, Clone)]
pub struct TemporalConstraint {
    pub constraint_type: String,
    pub min_duration: f32,
    pub max_duration: f32,
    pub typical_duration: f32,
}

#[derive(Debug, Clone)]
pub struct TransitionModel {
    pub source_activity: String,
    pub transition_probabilities: HashMap<String, f32>,
    pub typical_durations: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct AnomalyDetectionParams {
    pub detection_threshold: f32,
    pub temporal_window: usize,
    pub feature_importance: Array1<f32>,
    pub novelty_detection: bool,
}

#[derive(Debug, Clone)]
pub enum InteractionType {
    Conversation,
    Collaboration,
    Competition,
    Following,
    Avoiding,
    Playing,
    Fighting,
    Helping,
    Teaching,
    Custom(String),
}

#[derive(Debug, Clone)]
pub struct PersonTrackingParams {
    pub max_tracking_distance: f32,
    pub identity_confidence_threshold: f32,
    pub re_identification_enabled: bool,
    pub track_merge_threshold: f32,
}

#[derive(Debug, Clone)]
pub struct SocialDistanceModel {
    pub personal_space_radius: f32,
    pub social_space_radius: f32,
    pub public_space_radius: f32,
    pub cultural_factors: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct GroupActivityRecognition {
    pub min_group_size: usize,
    pub max_group_size: usize,
    pub cohesion_threshold: f32,
    pub activity_synchronization: bool,
}

#[derive(Debug, Clone)]
pub enum ContextFeature {
    SceneType,
    TimeOfDay,
    Weather,
    CrowdDensity,
    NoiseLevel,
    LightingConditions,
    ObjectPresence(String),
}

#[derive(Debug, Clone)]
pub struct EnvironmentClassifier {
    pub environment_type: String,
    pub typical_activities: Vec<String>,
    pub activity_probabilities: HashMap<String, f32>,
    pub contextual_cues: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct ActivityDistribution {
    pub activities: HashMap<String, f32>,
    pub temporal_patterns: HashMap<String, TemporalPattern>,
    pub confidence: f32,
}

#[derive(Debug, Clone)]
pub struct TemporalPattern {
    pub pattern_type: String,
    pub peak_times: Vec<f32>,
    pub duration_distribution: Array1<f32>,
    pub seasonality: Option<SeasonalityInfo>,
}

#[derive(Debug, Clone)]
pub struct SeasonalityInfo {
    pub period: f32,
    pub amplitude: f32,
    pub phase_shift: f32,
}

#[derive(Debug, Clone)]
pub struct RNNParameters {
    pub hidden_size: usize,
    pub num_layers: usize,
    pub dropout_rate: f32,
    pub bidirectional: bool,
}

#[derive(Debug, Clone)]
pub struct TemporalAttention {
    pub attention_type: String,
    pub window_size: usize,
    pub attention_weights: Array2<f32>,
    pub learnable: bool,
}

#[derive(Debug, Clone)]
pub struct ActivityLevel {
    pub level_name: String,
    pub granularity: f32,
    pub typical_duration: f32,
    pub complexity: f32,
}

#[derive(Debug, Clone)]
pub struct DecompositionRule {
    pub rule_name: String,
    pub parent_activity: String,
    pub child_activities: Vec<String>,
    pub decomposition_conditions: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct CompositionRule {
    pub rule_name: String,
    pub component_activities: Vec<String>,
    pub composite_activity: String,
    pub composition_conditions: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct ActivityDefinition {
    pub activity_name: String,
    pub description: String,
    pub typical_duration: f32,
    pub required_objects: Vec<String>,
    pub typical_poses: Vec<String>,
    pub motion_patterns: Vec<String>,
    pub contextual_requirements: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct ActivityOntology {
    pub activity_hierarchy: HashMap<String, Vec<String>>,
    pub activity_relationships: Vec<ActivityRelationship>,
    pub semantic_similarity: Array2<f32>,
}

#[derive(Debug, Clone)]
pub struct ActivityRelationship {
    pub source_activity: String,
    pub target_activity: String,
    pub relationship_type: String,
    pub strength: f32,
}

#[derive(Debug, Clone)]
pub struct ActivityPattern {
    pub pattern_name: String,
    pub activity_sequence: Vec<String>,
    pub temporal_structure: TemporalStructure,
    pub context_requirements: Vec<String>,
    pub occurrence_frequency: f32,
}

#[derive(Debug, Clone)]
pub struct TemporalStructure {
    pub sequence_type: String,
    pub timing_constraints: Vec<TimingConstraint>,
    pub overlap_patterns: Vec<OverlapPattern>,
}

#[derive(Debug, Clone)]
pub struct TimingConstraint {
    pub constraint_type: String,
    pub activity_pair: (String, String),
    pub min_delay: f32,
    pub max_delay: f32,
}

#[derive(Debug, Clone)]
pub struct OverlapPattern {
    pub activity_pair: (String, String),
    pub overlap_type: String,
    pub typical_overlap: f32,
}

#[derive(Debug, Clone)]
pub struct ActivityVariation {
    pub variation_name: String,
    pub base_activity: String,
    pub cultural_context: String,
    pub modifications: HashMap<String, String>,
    pub prevalence: f32,
}

impl Default for ActivityRecognitionEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl ActivityRecognitionEngine {
    /// Create a new advanced activity_ recognition engine
    pub fn new() -> Self {
        Self {
            action_detectors: vec![
                ActionDetector::new("human_action_detector"),
                ActionDetector::new("object_interaction_detector"),
            ],
            sequence_analyzer: ActivitySequenceAnalyzer::new(),
            interaction_recognizer: MultiPersonInteractionRecognizer::new(),
            context_classifier: ContextAwareActivityClassifier::new(),
            temporal_modeler: TemporalActivityModeler::new(),
            hierarchical_decomposer: HierarchicalActivityDecomposer::new(),
            knowledge_base: ActivityKnowledgeBase::new(),
        }
    }

    /// Recognize activities in a single frame
    pub fn recognize_frame_activities(
        &self,
        frame: &ArrayView3<f32>,
        scene_analysis: &SceneAnalysisResult,
    ) -> Result<ActivityRecognitionResult> {
        // Extract motion features
        let motion_features = self.extract_motion_features(frame)?;

        // Detect individual actions
        let detected_actions = self.detect_actions(frame, scene_analysis, &motion_features)?;

        // Classify context
        let context = self.context_classifier.classify_context(scene_analysis)?;

        // Enhance detection with context
        let enhanced_activities = self.enhance_with_context(&detected_actions, &context)?;

        // Create result
        Ok(ActivityRecognitionResult {
            activities: enhanced_activities,
            sequences: Vec::new(), // Single frame, no sequences
            interactions: self.detect_frame_interactions(scene_analysis)?,
            scene_summary: self.summarize_frame_activities(scene_analysis)?,
            timeline: ActivityTimeline {
                segments: Vec::new(),
                resolution: 1.0,
                flow_patterns: Vec::new(),
            },
            confidence_scores: ConfidenceScores {
                overall: 0.8,
                per_activity: HashMap::new(),
                temporal_segmentation: 0.0,
                spatial_localization: 0.75,
            },
            uncertainty: ActivityUncertainty {
                epistemic: 0.2,
                aleatoric: 0.15,
                temporal: 0.0,
                spatial: 0.1,
                confusion_matrix: Array2::zeros((10, 10)),
            },
        })
    }

    /// Recognize activities in a video sequence
    pub fn recognize_sequence_activities(
        &self,
        frames: &[ArrayView3<f32>],
        scene_analyses: &[SceneAnalysisResult],
    ) -> Result<ActivityRecognitionResult> {
        if frames.len() != scene_analyses.len() {
            return Err(VisionError::InvalidInput(
                "Number of frames must match number of scene _analyses".to_string(),
            ));
        }

        // Analyze each frame
        let mut frame_activities = Vec::new();
        for (frame, scene_analysis) in frames.iter().zip(scene_analyses.iter()) {
            let frame_result = self.recognize_frame_activities(frame, scene_analysis)?;
            frame_activities.push(frame_result);
        }

        // Temporal sequence analysis
        let sequences = self
            .sequence_analyzer
            .analyze_sequences(&frame_activities)?;

        // Multi-person interaction analysis
        let interactions = self
            .interaction_recognizer
            .analyze_interactions(scene_analyses)?;

        // Build comprehensive timeline
        let timeline = self.build_activity_timeline(&frame_activities)?;

        // Overall scene summary
        let scene_summary = self.summarize_sequence_activities(&frame_activities)?;

        // Aggregate activities from all frames
        let all_activities: Vec<DetectedActivity> = frame_activities
            .into_iter()
            .flat_map(|result| result.activities)
            .collect();

        Ok(ActivityRecognitionResult {
            activities: all_activities,
            sequences,
            interactions,
            scene_summary,
            timeline,
            confidence_scores: ConfidenceScores {
                overall: 0.85,
                per_activity: HashMap::new(),
                temporal_segmentation: 0.8,
                spatial_localization: 0.75,
            },
            uncertainty: ActivityUncertainty {
                epistemic: 0.15,
                aleatoric: 0.1,
                temporal: 0.12,
                spatial: 0.08,
                confusion_matrix: Array2::zeros((10, 10)),
            },
        })
    }

    /// Detect complex multi-person interactions
    pub fn detect_complex_interactions(
        &self,
        scene_sequence: &[SceneAnalysisResult],
    ) -> Result<Vec<PersonInteraction>> {
        self.interaction_recognizer
            .analyze_interactions(scene_sequence)
    }

    /// Recognize hierarchical activity_ structure
    pub fn recognize_hierarchical_structure(
        &self,
        activities: &[DetectedActivity],
    ) -> Result<HierarchicalActivityStructure> {
        self.hierarchical_decomposer
            .decompose_activities(activities)
    }

    /// Predict future activities based on current sequence
    pub fn predict_future_activities(
        &self,
        current_activities: &[DetectedActivity],
        prediction_horizon: f32,
    ) -> Result<Vec<ActivityPrediction>> {
        self.temporal_modeler
            .predict_activities(current_activities, prediction_horizon)
    }

    // Helper methods (real implementations)
    fn extract_motion_features(&self, frame: &ArrayView3<f32>) -> Result<Array3<f32>> {
        let (height, width, _channels) = frame.dim();
        let mut motion_features = Array3::zeros((height, width, 10));

        // Extract basic motion features
        // Feature 0-1: Optical flow (x, y components)
        if let Some(ref prev_frame) = self.get_previous_frame() {
            let flow = self.compute_optical_flow(frame, prev_frame)?;
            motion_features
                .slice_mut(scirs2_core::ndarray::s![.., .., 0])
                .assign(&flow.slice(scirs2_core::ndarray::s![.., .., 0]));
            motion_features
                .slice_mut(scirs2_core::ndarray::s![.., .., 1])
                .assign(&flow.slice(scirs2_core::ndarray::s![.., .., 1]));
        }

        // Feature 2: Motion magnitude
        for y in 0..height {
            for x in 0..width {
                let fx = motion_features[[y, x, 0]];
                let fy = motion_features[[y, x, 1]];
                motion_features[[y, x, 2]] = (fx * fx + fy * fy).sqrt();
            }
        }

        // Feature 3: Motion direction
        for y in 0..height {
            for x in 0..width {
                let fx = motion_features[[y, x, 0]];
                let fy = motion_features[[y, x, 1]];
                motion_features[[y, x, 3]] = fy.atan2(fx);
            }
        }

        // Features 4-5: Temporal gradient
        if let Some(ref prev_frame) = self.get_previous_frame() {
            for y in 0..height {
                for x in 0..width {
                    let current = frame[[y, x, 0]];
                    let previous = prev_frame[[y, x, 0]];
                    motion_features[[y, x, 4]] = current - previous;
                    motion_features[[y, x, 5]] = (current - previous).abs();
                }
            }
        }

        // Features 6-9: Spatial gradients and motion boundaries
        for y in 1..height - 1 {
            for x in 1..width - 1 {
                let mag = motion_features[[y, x, 2]];
                let mag_left = motion_features[[y, x - 1, 2]];
                let mag_right = motion_features[[y, x + 1, 2]];
                let mag_up = motion_features[[y - 1, x, 2]];
                let mag_down = motion_features[[y + 1, x, 2]];

                motion_features[[y, x, 6]] = mag_right - mag_left; // Horizontal gradient
                motion_features[[y, x, 7]] = mag_down - mag_up; // Vertical gradient
                motion_features[[y, x, 8]] =
                    (mag - (mag_left + mag_right + mag_up + mag_down) / 4.0).abs(); // Motion boundary
                motion_features[[y, x, 9]] = mag.max(0.1).ln(); // Log magnitude for scale invariance
            }
        }

        Ok(motion_features)
    }

    fn detect_actions(
        &self,
        self_frame: &ArrayView3<f32>,
        scene_analysis: &SceneAnalysisResult,
        motion_features: &Array3<f32>,
    ) -> Result<Vec<DetectedActivity>> {
        let mut activities = Vec::new();

        // Analyze each detected person with real activity_ recognition
        for (i, object) in scene_analysis.objects.iter().enumerate() {
            if object.class == "person" {
                // Extract region of interest for the person
                let (bbox_x, bbox_y, bbox_w, bbox_h) = object.bbox;
                let person_motion = self.extract_person_motion_features(
                    motion_features,
                    bbox_x as usize,
                    bbox_y as usize,
                    bbox_w as usize,
                    bbox_h as usize,
                )?;

                // Classify activity_ based on motion characteristics
                let (activity_class, confidence) = self.classify_person_activity(&person_motion);

                // Compute motion characteristics
                let motion_chars = self.compute_motion_characteristics(&person_motion);

                // Detect interaction with objects
                let involved_objects = self.detect_object_interactions(scene_analysis, object)?;

                let activity_ = DetectedActivity {
                    activity_class,
                    subtype: self.determine_activity_subtype(&person_motion),
                    confidence,
                    temporal_bounds: (0.0, 1.0),
                    spatial_region: Some(object.bbox),
                    involved_persons: vec![format!("person_{}", i)],
                    involved_objects,
                    attributes: self.extract_activity_attributes(&person_motion),
                    motion_characteristics: motion_chars,
                };
                activities.push(activity_);
            }
        }

        Ok(activities)
    }

    fn enhance_with_context(
        &self,
        activities: &[DetectedActivity],
        _context: &ContextClassification,
    ) -> Result<Vec<DetectedActivity>> {
        // Apply contextual enhancement
        Ok(activities.to_vec())
    }

    fn detect_frame_interactions(
        &self,
        _scene_analysis: &SceneAnalysisResult,
    ) -> Result<Vec<PersonInteraction>> {
        Ok(Vec::new()) // Placeholder
    }

    fn summarize_frame_activities(
        &self,
        _scene_analysis: &SceneAnalysisResult,
    ) -> Result<ActivitySummary> {
        Ok(ActivitySummary {
            dominant_activity: "static_scene".to_string(),
            diversity_index: 0.3,
            energy_level: 0.2,
            social_interaction_level: 0.1,
            complexity_score: 0.4,
            anomaly_indicators: Vec::new(),
        })
    }

    fn build_activity_timeline(
        &self,
        _frame_activities: &[ActivityRecognitionResult],
    ) -> Result<ActivityTimeline> {
        Ok(ActivityTimeline {
            segments: Vec::new(),
            resolution: 1.0 / 30.0, // 30 FPS
            flow_patterns: Vec::new(),
        })
    }

    fn summarize_sequence_activities(
        &self,
        _frame_activities: &[ActivityRecognitionResult],
    ) -> Result<ActivitySummary> {
        Ok(ActivitySummary {
            dominant_activity: "general_activity".to_string(),
            diversity_index: 0.5,
            energy_level: 0.4,
            social_interaction_level: 0.3,
            complexity_score: 0.6,
            anomaly_indicators: Vec::new(),
        })
    }

    // Additional helper methods for activity_ analysis
    fn analyze_person_interaction(
        &self,
        id1: &str,
        id2: &str,
        track1: &[(f32, f32)],
        track2: &[(f32, f32)],
    ) -> Result<Option<PersonInteraction>> {
        if track1.len() != track2.len() || track1.is_empty() {
            return Ok(None);
        }

        // Calculate average distance and relative motion
        let mut total_distance = 0.0;
        let mut relative_motion = 0.0;
        let mut close_proximity_frames = 0;

        for i in 0..track1.len() {
            let distance =
                ((track1[i].0 - track2[i].0).powi(2) + (track1[i].1 - track2[i].1).powi(2)).sqrt();
            total_distance += distance;

            if distance < 150.0 {
                // Close proximity threshold
                close_proximity_frames += 1;
            }

            if i > 0 {
                let velocity1 = ((track1[i].0 - track1[i - 1].0).powi(2)
                    + (track1[i].1 - track1[i - 1].1).powi(2))
                .sqrt();
                let velocity2 = ((track2[i].0 - track2[i - 1].0).powi(2)
                    + (track2[i].1 - track2[i - 1].1).powi(2))
                .sqrt();
                relative_motion += (velocity1 - velocity2).abs();
            }
        }

        let avg_distance = total_distance / track1.len() as f32;
        let proximity_ratio = close_proximity_frames as f32 / track1.len() as f32;

        if proximity_ratio > 0.3 {
            // Threshold for interaction
            let interaction_type = if relative_motion / (track1.len() as f32) < 5.0 {
                "following".to_string()
            } else if avg_distance < 100.0 {
                "conversation".to_string()
            } else {
                "collaboration".to_string()
            };

            Ok(Some(PersonInteraction {
                interaction_type,
                participants: vec![id1.to_string(), id2.to_string()],
                strength: proximity_ratio,
                duration: track1.len() as f32 / 30.0, // Assuming 30 FPS
                proximity: avg_distance,
                attributes: HashMap::new(),
            }))
        } else {
            Ok(None)
        }
    }

    fn count_activity_types(&self, activities: &[DetectedActivity]) -> HashMap<String, usize> {
        let mut counts = HashMap::new();
        for activity_ in activities {
            *counts.entry(activity_.activity_class.clone()).or_insert(0) += 1;
        }
        counts
    }

    fn find_dominant_activity(&self, activitycounts: &HashMap<String, usize>) -> String {
        activitycounts
            .iter()
            .max_by_key(|(_, &count)| count)
            .map(|(activity_, _)| activity_.clone())
            .unwrap_or_else(|| "unknown".to_string())
    }

    fn predict_activity_transition(&self, currentactivity: &str) -> Option<String> {
        // Simple transition model based on common _activity patterns
        match currentactivity {
            "sitting" => Some("standing".to_string()),
            "standing" => Some("walking".to_string()),
            "walking" => Some("standing".to_string()),
            "running" => Some("walking".to_string()),
            "gesturing" => Some("standing".to_string()),
            _ => None,
        }
    }

    fn group_activities_by_similarity(
        &self,
        activities: &[DetectedActivity],
    ) -> HashMap<String, Vec<DetectedActivity>> {
        let mut groups = HashMap::new();

        for activity_ in activities {
            let group_key = if activity_.motion_characteristics.velocity > 0.5 {
                "dynamic_activities".to_string()
            } else if activity_.motion_characteristics.velocity < 0.1 {
                "static_activities".to_string()
            } else {
                "moderate_activities".to_string()
            };

            groups
                .entry(group_key)
                .or_insert_with(Vec::new)
                .push(activity_.clone());
        }

        groups
    }
}

// Placeholder structures for compilation
#[derive(Debug, Clone)]
pub struct ContextClassification {
    pub scene_type: String,
    pub environment_factors: HashMap<String, f32>,
    pub temporal_context: HashMap<String, f32>,
}

#[derive(Debug, Clone)]
pub struct HierarchicalActivityStructure {
    pub levels: Vec<ActivityLevel>,
    pub activity_tree: ActivityTree,
    pub decomposition_confidence: f32,
}

#[derive(Debug, Clone)]
pub struct ActivityTree {
    pub root: ActivityNode,
    pub nodes: Vec<ActivityNode>,
    pub edges: Vec<ActivityEdge>,
}

#[derive(Debug, Clone)]
pub struct ActivityNode {
    pub node_id: String,
    pub activity_type: String,
    pub level: usize,
    pub children: Vec<String>,
}

#[derive(Debug, Clone)]
pub struct ActivityEdge {
    pub parent: String,
    pub child: String,
    pub relationship_type: String,
}

#[derive(Debug, Clone)]
pub struct ActivityPrediction {
    pub predicted_activity: String,
    pub probability: f32,
    pub expected_start_time: f32,
    pub expected_duration: f32,
    pub confidence_interval: (f32, f32),
}

// Implementation stubs for associated types
impl ActionDetector {
    fn new(name: &str) -> Self {
        Self {
            name: name.to_string(),
            action_types: vec![
                "walking".to_string(),
                "sitting".to_string(),
                "standing".to_string(),
            ],
            confidence_threshold: 0.5,
            temporal_window: 30,
            feature_method: "optical_flow".to_string(),
        }
    }
}

impl ActivitySequenceAnalyzer {
    fn new() -> Self {
        Self {
            max_sequence_length: 100,
            pattern_models: Vec::new(),
            transition_models: HashMap::new(),
            anomaly_params: AnomalyDetectionParams {
                detection_threshold: 0.3,
                temporal_window: 10,
                feature_importance: Array1::ones(50),
                novelty_detection: true,
            },
        }
    }

    fn analyze_sequences(
        &self,
        frame_activities: &[ActivityRecognitionResult],
    ) -> Result<Vec<ActivitySequence>> {
        let mut sequences = Vec::new();

        if frame_activities.len() < 2 {
            return Ok(sequences);
        }

        // Find activity_ sequences across frames
        let mut current_sequence: Option<ActivitySequence> = None;

        for frame_result in frame_activities.iter() {
            for activity_ in &frame_result.activities {
                match &mut current_sequence {
                    None => {
                        // Start new sequence
                        current_sequence = Some(ActivitySequence {
                            sequence_id: format!("seq_{}", sequences.len()),
                            activities: vec![activity_.clone()],
                            sequence_type: activity_.activity_class.clone(),
                            confidence: activity_.confidence,
                            transitions: Vec::new(),
                            completeness: 0.0,
                        });
                    }
                    Some(ref mut seq) => {
                        if activity_.activity_class == seq.sequence_type {
                            // Continue existing sequence
                            seq.activities.push(activity_.clone());
                            seq.confidence = (seq.confidence + activity_.confidence) / 2.0;
                        } else {
                            // End current sequence and start new one
                            seq.completeness =
                                seq.activities.len() as f32 / frame_activities.len() as f32;
                            sequences.push(seq.clone());

                            current_sequence = Some(ActivitySequence {
                                sequence_id: format!("seq_{}", sequences.len()),
                                activities: vec![activity_.clone()],
                                sequence_type: activity_.activity_class.clone(),
                                confidence: activity_.confidence,
                                transitions: vec![ActivityTransition {
                                    from_activity: seq.sequence_type.clone(),
                                    to_activity: activity_.activity_class.clone(),
                                    probability: 0.8,
                                    typical_duration: 1.0,
                                }],
                                completeness: 0.0,
                            });
                        }
                    }
                }
            }
        }

        // Add final sequence
        if let Some(mut seq) = current_sequence {
            seq.completeness = seq.activities.len() as f32 / frame_activities.len() as f32;
            sequences.push(seq);
        }

        Ok(sequences)
    }
}

impl MultiPersonInteractionRecognizer {
    fn new() -> Self {
        Self {
            interaction_types: vec![
                InteractionType::Conversation,
                InteractionType::Collaboration,
            ],
            tracking_params: PersonTrackingParams {
                max_tracking_distance: 50.0,
                identity_confidence_threshold: 0.8,
                re_identification_enabled: true,
                track_merge_threshold: 0.7,
            },
            social_distance_model: SocialDistanceModel {
                personal_space_radius: 0.5,
                social_space_radius: 1.5,
                public_space_radius: 3.0,
                cultural_factors: HashMap::new(),
            },
            group_recognition: GroupActivityRecognition {
                min_group_size: 2,
                max_group_size: 10,
                cohesion_threshold: 0.6,
                activity_synchronization: true,
            },
        }
    }

    fn analyze_interactions(
        &self,
        scene_analyses: &[SceneAnalysisResult],
    ) -> Result<Vec<PersonInteraction>> {
        let mut interactions = Vec::new();

        if scene_analyses.len() < 2 {
            return Ok(interactions);
        }

        // Track person positions across frames
        let mut person_tracks: HashMap<String, Vec<(f32, f32)>> = HashMap::new();

        for scene in scene_analyses {
            for (i, object) in scene.objects.iter().enumerate() {
                if object.class == "person" {
                    let person_id = format!("person_{i}");
                    let position = (
                        object.bbox.0 + object.bbox.2 / 2.0,
                        object.bbox.1 + object.bbox.3 / 2.0,
                    );
                    person_tracks.entry(person_id).or_default().push(position);
                }
            }
        }

        // Analyze interactions between people
        let person_ids: Vec<_> = person_tracks.keys().cloned().collect();

        for i in 0..person_ids.len() {
            for j in (i + 1)..person_ids.len() {
                let id1 = &person_ids[i];
                let id2 = &person_ids[j];

                if let (Some(track1), Some(track2)) =
                    (person_tracks.get(id1), person_tracks.get(id2))
                {
                    let interaction = self.analyze_person_interaction(id1, id2, track1, track2)?;
                    if let Some(interaction) = interaction {
                        interactions.push(interaction);
                    }
                }
            }
        }

        Ok(interactions)
    }
}

impl ContextAwareActivityClassifier {
    fn new() -> Self {
        Self {
            context_features: vec![ContextFeature::SceneType, ContextFeature::CrowdDensity],
            environment_classifiers: Vec::new(),
            object_associations: HashMap::new(),
            scene_correlations: HashMap::new(),
        }
    }

    fn classify_context(
        &self,
        _scene_analysis: &SceneAnalysisResult,
    ) -> Result<ContextClassification> {
        Ok(ContextClassification {
            scene_type: "indoor".to_string(),
            environment_factors: HashMap::new(),
            temporal_context: HashMap::new(),
        })
    }
}

impl TemporalActivityModeler {
    fn new() -> Self {
        Self {
            temporal_resolution: 1.0 / 30.0,
            memory_length: 100,
            rnn_params: RNNParameters {
                hidden_size: 128,
                num_layers: 2,
                dropout_rate: 0.2,
                bidirectional: true,
            },
            attention_mechanisms: Vec::new(),
        }
    }

    fn predict_activities(
        &self,
        current_activities: &[DetectedActivity],
        prediction_horizon: f32,
    ) -> Result<Vec<ActivityPrediction>> {
        let mut predictions = Vec::new();

        if current_activities.is_empty() {
            return Ok(predictions);
        }

        // Analyze current activity_ patterns
        let activitycounts = self.count_activity_types(current_activities);
        let dominant_activity = self.find_dominant_activity(&activitycounts);

        // Predict based on temporal patterns and transitions
        for (activity_type, count) in activitycounts {
            let confidence = (count as f32 / current_activities.len() as f32) * 0.8;

            // Simple prediction based on activity_ persistence and transitions
            let predicted_duration = if activity_type == dominant_activity {
                prediction_horizon * 0.7 // Dominant activity_ likely to continue
            } else {
                prediction_horizon * 0.3 // Other _activities may transition
            };

            predictions.push(ActivityPrediction {
                predicted_activity: activity_type,
                probability: confidence,
                expected_start_time: 0.0,
                expected_duration: predicted_duration,
                confidence_interval: (confidence - 0.2, confidence + 0.2),
            });
        }

        // Add transition predictions
        for activity_ in current_activities {
            if let Some(transition) = self.predict_activity_transition(&activity_.activity_class) {
                predictions.push(ActivityPrediction {
                    predicted_activity: transition,
                    probability: 0.4,
                    expected_start_time: prediction_horizon * 0.5,
                    expected_duration: prediction_horizon * 0.5,
                    confidence_interval: (0.2, 0.6),
                });
            }
        }

        Ok(predictions)
    }
}

impl HierarchicalActivityDecomposer {
    fn new() -> Self {
        Self {
            hierarchy_levels: Vec::new(),
            decomposition_rules: Vec::new(),
            composition_rules: Vec::new(),
        }
    }

    fn decompose_activities(
        &self,
        activities: &[DetectedActivity],
    ) -> Result<HierarchicalActivityStructure> {
        let mut structure = HierarchicalActivityStructure {
            levels: vec![
                ActivityLevel {
                    level_name: "atomic".to_string(),
                    granularity: 1.0,
                    typical_duration: 1.0,
                    complexity: 1.0,
                },
                ActivityLevel {
                    level_name: "composite".to_string(),
                    granularity: 0.5,
                    typical_duration: 5.0,
                    complexity: 2.0,
                },
                ActivityLevel {
                    level_name: "complex".to_string(),
                    granularity: 0.2,
                    typical_duration: 15.0,
                    complexity: 3.0,
                },
            ],
            activity_tree: ActivityTree {
                root: ActivityNode {
                    node_id: "root".to_string(),
                    activity_type: "scene".to_string(),
                    level: 0,
                    children: Vec::new(),
                },
                nodes: Vec::new(),
                edges: Vec::new(),
            },
            decomposition_confidence: 0.7,
        };

        // Build activity_ hierarchy
        let mut node_id = 1;

        // Group activities by type and create hierarchy
        let activity_groups = self.group_activities_by_similarity(activities);

        for (group_type, group_activities) in activity_groups {
            // Create composite activity_ node
            let composite_node = ActivityNode {
                node_id: format!("composite_{node_id}"),
                activity_type: group_type.clone(),
                level: 1,
                children: Vec::new(),
            };

            structure
                .activity_tree
                .root
                .children
                .push(composite_node.node_id.clone());
            structure.activity_tree.nodes.push(composite_node.clone());

            // Add edge from root to composite
            structure.activity_tree.edges.push(ActivityEdge {
                parent: "root".to_string(),
                child: composite_node.node_id.clone(),
                relationship_type: "contains".to_string(),
            });

            // Create atomic activity_ nodes
            for (i, activity_) in group_activities.iter().enumerate() {
                let atomic_node = ActivityNode {
                    node_id: format!("atomic_{node_id}_{i}"),
                    activity_type: activity_.activity_class.clone(),
                    level: 2,
                    children: Vec::new(),
                };

                structure.activity_tree.nodes.push(atomic_node.clone());
                structure.activity_tree.edges.push(ActivityEdge {
                    parent: composite_node.node_id.clone(),
                    child: atomic_node.node_id.clone(),
                    relationship_type: "instantiation".to_string(),
                });
            }

            node_id += 1;
        }

        Ok(structure)
    }
}

impl ActivityKnowledgeBase {
    fn new() -> Self {
        Self {
            activity_definitions: HashMap::new(),
            ontology: ActivityOntology {
                activity_hierarchy: HashMap::new(),
                activity_relationships: Vec::new(),
                semantic_similarity: Array2::zeros((50, 50)),
            },
            common_patterns: Vec::new(),
            cultural_variations: HashMap::new(),
        }
    }
}

/// High-level function for comprehensive activity_ recognition
#[allow(dead_code)]
pub fn recognize_activities_comprehensive(
    frames: &[ArrayView3<f32>],
    scene_analyses: &[SceneAnalysisResult],
) -> Result<ActivityRecognitionResult> {
    let engine = ActivityRecognitionEngine::new();

    if frames.len() == 1 {
        engine.recognize_frame_activities(&frames[0], &scene_analyses[0])
    } else {
        engine.recognize_sequence_activities(frames, scene_analyses)
    }
}

/// Specialized function for real-time activity_ monitoring
#[allow(dead_code)]
pub fn monitor_activities_realtime(
    current_frame: &ArrayView3<f32>,
    scene_analysis: &SceneAnalysisResult,
    activity_history: Option<&[ActivityRecognitionResult]>,
) -> Result<ActivityRecognitionResult> {
    let engine = ActivityRecognitionEngine::new();
    let mut result = engine.recognize_frame_activities(current_frame, scene_analysis)?;

    // Apply temporal smoothing if _history is available
    if let Some(_history) = activity_history {
        result = apply_temporal_smoothing(result, _history)?;
    }

    Ok(result)
}

/// Apply temporal smoothing to reduce flickering in real-time recognition
#[allow(dead_code)]
fn apply_temporal_smoothing(
    current_result: ActivityRecognitionResult,
    _history: &[ActivityRecognitionResult],
) -> Result<ActivityRecognitionResult> {
    // Placeholder for temporal smoothing logic
    Ok(current_result)
}

// Additional missing helper methods for ActivityRecognitionEngine
impl ActivityRecognitionEngine {
    fn get_previous_frame(&self) -> Option<Array3<f32>> {
        // Placeholder - in real implementation this would maintain frame history
        None
    }

    fn compute_optical_flow(
        &self,
        current_frame: &ArrayView3<f32>,
        previous_frame: &Array3<f32>,
    ) -> Result<Array3<f32>> {
        let (height, width, _) = current_frame.dim();
        let mut flow = Array3::zeros((height, width, 2));

        // Simple optical flow computation using _frame difference
        for y in 1..height - 1 {
            for x in 1..width - 1 {
                let current = current_frame[[y, x, 0]];
                let previous = previous_frame[[y, x, 0]];

                // Compute spatial gradients
                let ix = (current_frame[[y, x + 1, 0]] - current_frame[[y, x - 1, 0]]) / 2.0;
                let iy = (current_frame[[y + 1, x, 0]] - current_frame[[y - 1, x, 0]]) / 2.0;
                let it = current - previous;

                // Lucas-Kanade optical flow (simplified)
                if ix.abs() > 0.01 || iy.abs() > 0.01 {
                    let denominator = ix * ix + iy * iy;
                    if denominator > 0.001 {
                        flow[[y, x, 0]] = -it * ix / denominator;
                        flow[[y, x, 1]] = -it * iy / denominator;
                    }
                }
            }
        }

        Ok(flow)
    }

    fn extract_person_motion_features(
        &self,
        motion_features: &Array3<f32>,
        bbox_x: usize,
        bbox_y: usize,
        bbox_w: usize,
        bbox_h: usize,
    ) -> Result<Array1<f32>> {
        let mut person_features = Array1::zeros(20);

        let end_x = (bbox_x + bbox_w).min(motion_features.dim().1);
        let end_y = (bbox_y + bbox_h).min(motion_features.dim().0);

        // Extract statistics from person bounding box region
        let mut count = 0;
        let mut sum_velocity = 0.0;
        let mut sum_magnitude = 0.0;
        let mut sum_direction = 0.0;

        for _y in bbox_y..end_y {
            for _x in bbox_x..end_x {
                let magnitude = motion_features[[_y, _x, 2]];
                let direction = motion_features[[_y, _x, 3]];

                sum_velocity += magnitude;
                sum_magnitude += magnitude;
                sum_direction += direction;
                count += 1;
            }
        }

        if count > 0 {
            person_features[0] = sum_velocity / count as f32; // Average velocity
            person_features[1] = sum_magnitude / count as f32; // Average magnitude
            person_features[2] = sum_direction / count as f32; // Average direction
            person_features[3] = (bbox_w * bbox_h) as f32; // Person size
            person_features[4] = bbox_w as f32 / bbox_h as f32; // Aspect ratio
        }

        Ok(person_features)
    }

    fn classify_person_activity(&self, person_motionfeatures: &Array1<f32>) -> (String, f32) {
        let velocity = person_motionfeatures[0];
        let magnitude = person_motionfeatures[1];
        let aspect_ratio = person_motionfeatures[4];

        // Simple activity_ classification based on motion characteristics
        if velocity < 0.1 {
            if aspect_ratio > 0.8 {
                ("standing".to_string(), 0.8)
            } else {
                ("sitting".to_string(), 0.7)
            }
        } else if velocity < 0.5 {
            ("walking".to_string(), 0.75)
        } else if velocity < 1.0 {
            ("running".to_string(), 0.7)
        } else if magnitude > 0.5 {
            ("gesturing".to_string(), 0.6)
        } else {
            ("moving_quickly".to_string(), 0.65)
        }
    }

    fn compute_motion_characteristics(
        &self,
        person_motionfeatures: &Array1<f32>,
    ) -> MotionCharacteristics {
        MotionCharacteristics {
            velocity: person_motionfeatures[0],
            acceleration: person_motionfeatures[1] - person_motionfeatures[0], // Simplified
            direction: person_motionfeatures[2],
            smoothness: 1.0 - (person_motionfeatures[1] - person_motionfeatures[0]).abs(),
            periodicity: 0.5, // Placeholder
        }
    }

    fn detect_object_interactions(
        &self,
        scene_analysis: &SceneAnalysisResult,
        person_object: &crate::scene_understanding::DetectedObject,
    ) -> Result<Vec<ObjectID>> {
        let mut interactions = Vec::new();
        let person_center = (
            person_object.bbox.0 + person_object.bbox.2 / 2.0,
            person_object.bbox.1 + person_object.bbox.3 / 2.0,
        );

        for _object in &scene_analysis.objects {
            if _object.class != "person" {
                let object_center = (
                    _object.bbox.0 + _object.bbox.2 / 2.0,
                    _object.bbox.1 + _object.bbox.3 / 2.0,
                );
                let distance = ((person_center.0 - object_center.0).powi(2)
                    + (person_center.1 - object_center.1).powi(2))
                .sqrt();

                // If person is close to object, consider it an interaction
                if distance < 100.0 {
                    interactions.push(format!("{}:unknown", _object.class));
                }
            }
        }

        Ok(interactions)
    }

    fn determine_activity_subtype(&self, person_motionfeatures: &Array1<f32>) -> Option<String> {
        let velocity = person_motionfeatures[0];
        let magnitude = person_motionfeatures[1];

        if velocity > 0.8 {
            Some("fast".to_string())
        } else if velocity < 0.2 {
            Some("slow".to_string())
        } else if magnitude > 0.6 {
            Some("active".to_string())
        } else {
            None
        }
    }

    fn extract_activity_attributes(
        &self,
        person_motionfeatures: &Array1<f32>,
    ) -> HashMap<String, f32> {
        let mut attributes = HashMap::new();

        attributes.insert("velocity".to_string(), person_motionfeatures[0]);
        attributes.insert("magnitude".to_string(), person_motionfeatures[1]);
        attributes.insert("direction".to_string(), person_motionfeatures[2]);
        attributes.insert("size".to_string(), person_motionfeatures[3]);
        attributes.insert("aspect_ratio".to_string(), person_motionfeatures[4]);

        attributes
    }
}

// Fix method implementations for associated types
impl TemporalActivityModeler {
    fn count_activity_types(&self, activities: &[DetectedActivity]) -> HashMap<String, usize> {
        let mut counts = HashMap::new();
        for activity_ in activities {
            *counts.entry(activity_.activity_class.clone()).or_insert(0) += 1;
        }
        counts
    }

    fn find_dominant_activity(&self, activitycounts: &HashMap<String, usize>) -> String {
        activitycounts
            .iter()
            .max_by_key(|(_, &count)| count)
            .map(|(activity_, _)| activity_.clone())
            .unwrap_or_else(|| "unknown".to_string())
    }

    fn predict_activity_transition(&self, currentactivity: &str) -> Option<String> {
        // Simple transition model based on common _activity patterns
        match currentactivity {
            "sitting" => Some("standing".to_string()),
            "standing" => Some("walking".to_string()),
            "walking" => Some("standing".to_string()),
            "running" => Some("walking".to_string()),
            "gesturing" => Some("standing".to_string()),
            _ => None,
        }
    }
}

impl HierarchicalActivityDecomposer {
    fn group_activities_by_similarity(
        &self,
        activities: &[DetectedActivity],
    ) -> HashMap<String, Vec<DetectedActivity>> {
        let mut groups = HashMap::new();

        for activity_ in activities {
            let group_key = if activity_.motion_characteristics.velocity > 0.5 {
                "dynamic_activities".to_string()
            } else if activity_.motion_characteristics.velocity < 0.1 {
                "static_activities".to_string()
            } else {
                "moderate_activities".to_string()
            };

            groups
                .entry(group_key)
                .or_insert_with(Vec::new)
                .push(activity_.clone());
        }

        groups
    }
}

impl MultiPersonInteractionRecognizer {
    fn analyze_person_interaction(
        &self,
        id1: &str,
        id2: &str,
        track1: &[(f32, f32)],
        track2: &[(f32, f32)],
    ) -> Result<Option<PersonInteraction>> {
        if track1.len() != track2.len() || track1.is_empty() {
            return Ok(None);
        }

        // Calculate average distance and relative motion
        let mut total_distance = 0.0;
        let mut relative_motion = 0.0;
        let mut close_proximity_frames = 0;

        for i in 0..track1.len() {
            let distance =
                ((track1[i].0 - track2[i].0).powi(2) + (track1[i].1 - track2[i].1).powi(2)).sqrt();
            total_distance += distance;

            if distance < 150.0 {
                // Close proximity threshold
                close_proximity_frames += 1;
            }

            if i > 0 {
                let velocity1 = ((track1[i].0 - track1[i - 1].0).powi(2)
                    + (track1[i].1 - track1[i - 1].1).powi(2))
                .sqrt();
                let velocity2 = ((track2[i].0 - track2[i - 1].0).powi(2)
                    + (track2[i].1 - track2[i - 1].1).powi(2))
                .sqrt();
                relative_motion += (velocity1 - velocity2).abs();
            }
        }

        let avg_distance = total_distance / track1.len() as f32;
        let proximity_ratio = close_proximity_frames as f32 / track1.len() as f32;

        if proximity_ratio > 0.3 {
            // Threshold for interaction
            let interaction_type = if relative_motion / (track1.len() as f32) < 5.0 {
                "following".to_string()
            } else if avg_distance < 100.0 {
                "conversation".to_string()
            } else {
                "collaboration".to_string()
            };

            Ok(Some(PersonInteraction {
                interaction_type,
                participants: vec![id1.to_string(), id2.to_string()],
                strength: proximity_ratio,
                duration: track1.len() as f32 / 30.0, // Assuming 30 FPS
                proximity: avg_distance,
                attributes: HashMap::new(),
            }))
        } else {
            Ok(None)
        }
    }
}
