构建AI智能体：YOLO多模态智能感知系统：从理论到实践的实时目标检测探讨

class MultimodalFusionTheory:
    """多模态融合理论基础"""
    
    def data_level_fusion(self, visual_data, thermal_data, lidar_data):
        """
        数据级融合 - 早期融合
        优点：信息损失最小，保留原始特征
        缺点：数据对齐困难，计算复杂度高
        """
        # 时空对齐
        aligned_data = self._spatiotemporal_alignment(
            visual_data, thermal_data, lidar_data
        )
        
        # 数据融合
        fused_data = self._early_fusion_strategy(aligned_data)
        return fused_data
    
    def feature_level_fusion(self, visual_features, thermal_features, depth_features):
        """
        特征级融合 - 中期融合
        优点：平衡信息保留和计算效率
        缺点：特征对齐和权重分配复杂
        """
        # 特征对齐和归一化
        normalized_features = self._feature_normalization([
            visual_features, thermal_features, depth_features
        ])
        
        # 自适应特征融合
        fusion_weights = self._calculate_fusion_weights(normalized_features)
        fused_features = self._weighted_fusion(normalized_features, fusion_weights)
        
        return fused_features
    
    def decision_level_fusion(self, visual_detections, thermal_detections, radar_detections):
        """
        决策级融合 - 晚期融合
        优点：灵活性高，容错性强
        缺点：信息损失最大，依赖各模态性能
        """
        # 检测结果对齐
        aligned_detections = self._detection_association([
            visual_detections, thermal_detections, radar_detections
        ])
        
        # 置信度融合
        fused_detections = self._confidence_fusion(aligned_detections)
        return fused_detections
    
    def hybrid_fusion_strategy(self, data_dict):
        """
        混合融合策略 - 结合各级融合优势
        """
        # 关键区域数据级融合
        roi_data_fusion = self.data_level_fusion(
            data_dict['visual_roi'], 
            data_dict['thermal_roi'],
            data_dict['lidar_roi']
        )
        
        # 全局特征级融合
        global_feature_fusion = self.feature_level_fusion(
            data_dict['visual_features'],
            data_dict['thermal_features'], 
            data_dict['depth_features']
        )
        
        # 最终决策级融合
        final_fusion = self.decision_level_fusion(
            data_dict['visual_detections'],
            data_dict['thermal_detections'],
            data_dict['radar_detections']
        )
        
        return {
            'roi_fusion': roi_data_fusion,
            'feature_fusion': global_feature_fusion,
            'decision_fusion': final_fusion
        }

class BehaviorAnalysisModule:
    """行为分析模块"""
    
    def __init__(self):
        self.behavior_models = {}
        self.temporal_analyzer = TemporalAnalyzer()
        self.interaction_analyzer = InteractionAnalyzer()
        self.intent_predictor = IntentPredictor()
        
        # 初始化行为模型
        self._initialize_behavior_models()
    
    def _initialize_behavior_models(self):
        """初始化行为分析模型"""
        self.behavior_models = {
            'pedestrian': {
                'walking': WalkingBehaviorModel(),
                'running': RunningBehaviorModel(),
                'standing': StandingBehaviorModel(),
                'crossing': CrossingBehaviorModel()
            },
            'vehicle': {
                'moving': MovingVehicleModel(),
                'stopped': StoppedVehicleModel(),
                'turning': TurningVehicleModel(),
                'reversing': ReversingVehicleModel()
            },
            'group': {
                'gathering': GroupGatheringModel(),
                'dispersing': GroupDispersingModel(),
                'interacting': GroupInteractionModel()
            }
        }
    
    async def analyze_behaviors(self, 
                              detections: List[Dict], 
                              multimodal_data: Dict) -> Dict[str, Any]:
        """
        分析目标行为
        Args:
            detections: 目标检测结果
            multimodal_data: 多模态数据
        Returns:
            行为分析结果
        """
        print(" 开始行为分析...")
        
        behavior_results = {
            'individual_behaviors': {},
            'group_behaviors': {},
            'interactions': {},
            'anomalies': {},
            'predictions': {}
        }
        
        # 1. 个体行为分析
        individual_behaviors = await self._analyze_individual_behaviors(
            detections, multimodal_data
        )
        behavior_results['individual_behaviors'] = individual_behaviors
        
        # 2. 群体行为分析
        group_behaviors = await self._analyze_group_behaviors(
            detections, multimodal_data
        )
        behavior_results['group_behaviors'] = group_behaviors
        
        # 3. 交互关系分析
        interactions = await self._analyze_interactions(
            detections, multimodal_data
        )
        behavior_results['interactions'] = interactions
        
        # 4. 异常行为检测
        anomalies = await self._detect_anomalies(
            detections, multimodal_data
        )
        behavior_results['anomalies'] = anomalies
        
        # 5. 行为预测
        predictions = await self._predict_future_behaviors(
            detections, multimodal_data
        )
        behavior_results['predictions'] = predictions
        
        return behavior_results
    
    async def _analyze_individual_behaviors(self, 
                                          detections: List[Dict],
                                          multimodal_data: Dict) -> Dict:
        """分析个体行为"""
        individual_results = {}
        
        for detection in detections:
            obj_id = detection['id']
            obj_class = detection['class']
            bbox = detection['bbox']
            track_history = detection.get('track_history', [])
            
            # 根据目标类型选择相应的行为模型
            if obj_class in self.behavior_models:
                behavior_model = self._select_behavior_model(obj_class, track_history)
                
                # 提取行为特征
                behavior_features = self._extract_behavior_features(
                    detection, multimodal_data
                )
                
                # 行为分类
                behavior_type, confidence = behavior_model.classify_behavior(
                    behavior_features
                )
                
                individual_results[obj_id] = {
                    'behavior_type': behavior_type,
                    'confidence': confidence,
                    'features': behavior_features,
                    'model_used': behavior_model.__class__.__name__
                }
        
        return individual_results
    
    async def _analyze_group_behaviors(self, 
                                     detections: List[Dict],
                                     multimodal_data: Dict) -> Dict:
        """分析群体行为"""
        # 群体检测和分组
        groups = self._detect_groups(detections)
        
        group_behaviors = {}
        for group_id, group_members in groups.items():
            # 分析群体动态
            group_dynamics = self._analyze_group_dynamics(group_members)
            
            # 群体行为分类
            group_behavior = self._classify_group_behavior(group_dynamics)
            
            group_behaviors[group_id] = {
                'members': [det['id'] for det in group_members],
                'behavior_type': group_behavior['type'],
                'cohesion': group_behavior['cohesion'],
                'collective_motion': group_behavior['motion_pattern']
            }
        
        return group_behaviors
    
    async def _analyze_interactions(self, 
                                  detections: List[Dict],
                                  multimodal_data: Dict) -> Dict:
        """分析目标间交互关系"""
        interactions = {}
        
        # 构建交互图
        interaction_graph = self._build_interaction_graph(detections)
        
        for i, detection_i in enumerate(detections):
            for j, detection_j in enumerate(detections[i+1:], i+1):
                # 计算交互强度
                interaction_strength = self._calculate_interaction_strength(
                    detection_i, detection_j, multimodal_data
                )
                
                if interaction_strength > 0.1:  # 阈值
                    interaction_type = self._classify_interaction_type(
                        detection_i, detection_j, interaction_strength
                    )
                    
                    interaction_key = f"{detection_i['id']}-{detection_j['id']}"
                    interactions[interaction_key] = {
                        'participants': [detection_i['id'], detection_j['id']],
                        'type': interaction_type,
                        'strength': interaction_strength,
                        'distance': self._calculate_distance(detection_i, detection_j)
                    }
        
        return interactions
    
    async def _detect_anomalies(self, 
                              detections: List[Dict],
                              multimodal_data: Dict) -> Dict:
        """检测异常行为"""
        anomalies = {}
        
        for detection in detections:
            # 基于历史行为的异常检测
            behavior_history = detection.get('behavior_history', [])
            current_behavior = detection.get('current_behavior')
            
            if behavior_history and current_behavior:
                anomaly_score = self._calculate_anomaly_score(
                    current_behavior, behavior_history
                )
                
                if anomaly_score > 0.7:  # 异常阈值
                    anomalies[detection['id']] = {
                        'type': 'behavior_anomaly',
                        'score': anomaly_score,
                        'description': self._generate_anomaly_description(
                            current_behavior, behavior_history
                        )
                    }
        
        # 环境上下文异常检测
        contextual_anomalies = self._detect_contextual_anomalies(
            detections, multimodal_data
        )
        anomalies.update(contextual_anomalies)
        
        return anomalies
    
    async def _predict_future_behaviors(self, 
                                      detections: List[Dict],
                                      multimodal_data: Dict) -> Dict:
        """预测未来行为"""
        predictions = {}
        
        for detection in detections:
            # 轨迹预测
            predicted_trajectory = self._predict_trajectory(
                detection, multimodal_data
            )
            
            # 意图预测
            predicted_intent = self._predict_intent(
                detection, multimodal_data
            )
            
            # 风险评估
            risk_assessment = self._assess_behavior_risk(
                detection, predicted_trajectory, predicted_intent
            )
            
            predictions[detection['id']] = {
                'predicted_trajectory': predicted_trajectory,
                'predicted_intent': predicted_intent,
                'risk_level': risk_assessment['level'],
                'risk_reason': risk_assessment['reason'],
                'confidence': risk_assessment['confidence']
            }
        
        return predictions
    
    def _extract_behavior_features(self, detection: Dict, multimodal_data: Dict) -> Dict:
        """提取行为特征"""
        features = {
            'motion_features': {
                'velocity': self._calculate_velocity(detection),
                'acceleration': self._calculate_acceleration(detection),
                'direction': self._calculate_direction(detection),
                'motion_consistency': self._calculate_motion_consistency(detection)
            },
            'spatial_features': {
                'position': detection['bbox'][:2],  # 中心点
                'size': detection['bbox'][2:],     # 宽高
                'proximity_to_others': self._calculate_proximity(detection),
                'environment_context': self._get_environment_context(detection, multimodal_data)
            },
            'temporal_features': {
                'duration_present': self._calculate_presence_duration(detection),
                'behavior_history': detection.get('behavior_history', []),
                'periodic_patterns': self._detect_periodic_patterns(detection)
            }
        }
        
        return features

async def _analyze_individual_behaviors(self, detections, multimodal_data):
    for detection in detections:
        obj_class = detection['class']
        track_history = detection.get('track_history', [])
        
        if obj_class in self.behavior_models:
            behavior_model = self._select_behavior_model(obj_class, track_history)
            behavior_features = self._extract_behavior_features(detection, multimodal_data)
            behavior_type, confidence = behavior_model.classify_behavior(behavior_features)

async def _analyze_group_behaviors(self, detections, multimodal_data):
    groups = self._detect_groups(detections)
    for group_id, group_members in groups.items():
        group_dynamics = self._analyze_group_dynamics(group_members)
        group_behavior = self._classify_group_behavior(group_dynamics)

async def _analyze_interactions(self, detections, multimodal_data):
    interaction_graph = self._build_interaction_graph(detections)
    for i, detection_i in enumerate(detections):
        for j, detection_j in enumerate(detections[i+1:], i+1):
            interaction_strength = self._calculate_interaction_strength(
                detection_i, detection_j, multimodal_data
            )
            if interaction_strength > 0.1:
                interaction_type = self._classify_interaction_type(...)

async def _detect_anomalies(self, detections, multimodal_data):
    for detection in detections:
        behavior_history = detection.get('behavior_history', [])
        current_behavior = detection.get('current_behavior')
        
        if behavior_history and current_behavior:
            anomaly_score = self._calculate_anomaly_score(
                current_behavior, behavior_history
            )
            if anomaly_score > 0.7:
                # 标记为异常

async def _predict_future_behaviors(self, detections, multimodal_data):
    for detection in detections:
        predicted_trajectory = self._predict_trajectory(detection, multimodal_data)
        predicted_intent = self._predict_intent(detection, multimodal_data)
        risk_assessment = self._assess_behavior_risk(
            detection, predicted_trajectory, predicted_intent
        )

class TemporalAnalyzer:
    """时空分析器"""
    
    def __init__(self, window_size: int = 30):
        self.window_size = window_size
        self.temporal_patterns = {}
    
    def analyze_temporal_patterns(self, track_history: List) -> Dict:
        """分析时空模式"""
        if len(track_history) < 2:
            return {}
        
        # 提取时空特征
        positions = [track['position'] for track in track_history]
        timestamps = [track['timestamp'] for track in track_history]
        
        analysis_results = {
            'trajectory_analysis': self._analyze_trajectory(positions, timestamps),
            'motion_patterns': self._analyze_motion_patterns(positions, timestamps),
            'periodicity': self._detect_periodicity(positions, timestamps),
            'stationarity': self._analyze_stationarity(positions)
        }
        
        return analysis_results
    
    def _analyze_trajectory(self, positions: List, timestamps: List) -> Dict:
        """分析运动轨迹"""
        if len(positions) < 2:
            return {}
        
        # 计算轨迹特征
        displacements = []
        directions = []
        speeds = []
        
        for i in range(1, len(positions)):
            pos1 = positions[i-1]
            pos2 = positions[i]
            time_diff = timestamps[i] - timestamps[i-1]
            
            # 位移
            displacement = np.linalg.norm(np.array(pos2) - np.array(pos1))
            displacements.append(displacement)
            
            # 方向
            direction = np.arctan2(pos2[1]-pos1[1], pos2[0]-pos1[0])
            directions.append(direction)
            
            # 速度
            speed = displacement / time_diff if time_diff > 0 else 0
            speeds.append(speed)
        
        return {
            'total_distance': sum(displacements),
            'average_speed': np.mean(speeds) if speeds else 0,
            'speed_variance': np.var(speeds) if speeds else 0,
            'direction_consistency': self._calculate_direction_consistency(directions),
            'trajectory_smoothness': self._calculate_trajectory_smoothness(positions)
        }
    
    def predict_future_positions(self, 
                               current_state: Dict, 
                               history: List,
                               prediction_steps: int = 10) -> List:
        """预测未来位置"""
        if len(history) < 5:  # 需要足够的历史数据
            return self._simple_extrapolation(current_state, prediction_steps)
        
        # 使用卡尔曼滤波进行预测
        predictions = self._kalman_filter_prediction(current_state, history, prediction_steps)
        
        # 使用LSTM进行深度学习预测
        lstm_predictions = self._lstm_prediction(history, prediction_steps)
        
        # 融合预测结果
        fused_predictions = self._fuse_predictions(predictions, lstm_predictions)
        
        return fused_predictions
    
    def _kalman_filter_prediction(self, current_state: Dict, history: List, steps: int) -> List:
        """卡尔曼滤波预测"""
        # 初始化卡尔曼滤波器
        kf = cv2.KalmanFilter(4, 2)  # 4状态变量，2观测变量
        
        # 状态转移矩阵 [x, y, vx, vy]
        kf.transitionMatrix = np.array([
            [1, 0, 1, 0],
            [0, 1, 0, 1],
            [0, 0, 1, 0],
            [0, 0, 0, 1]
        ], np.float32)
        
        # 观测矩阵
        kf.measurementMatrix = np.array([
            [1, 0, 0, 0],
            [0, 1, 0, 0]
        ], np.float32)
        
        # 从历史数据学习过程噪声和观测噪声
        self._learn_kalman_parameters(kf, history)
        
        # 设置初始状态
        current_pos = current_state['position']
        current_vel = current_state.get('velocity', [0, 0])
        kf.statePost = np.array([current_pos[0], current_pos[1], 
                               current_vel[0], current_vel[1]], np.float32)
        
        # 进行预测
        predictions = []
        for _ in range(steps):
            prediction = kf.predict()
            predictions.append([prediction[0], prediction[1]])
        
        return predictions

class TemporalAnalyzer:
    def __init__(self, window_size: int = 30):
        self.window_size = window_size
        self.temporal_patterns = {}

def analyze_temporal_patterns(self, track_history: List) -> Dict:
    if len(track_history) < 2:
        return {}
    
    positions = [track['position'] for track in track_history]
    timestamps = [track['timestamp'] for track in track_history]
    
    analysis_results = {
        'trajectory_analysis': self._analyze_trajectory(positions, timestamps),
        'motion_patterns': self._analyze_motion_patterns(positions, timestamps),
        'periodicity': self._detect_periodicity(positions, timestamps),
        'stationarity': self._analyze_stationarity(positions)
    }
    
    return analysis_results

def _analyze_trajectory(self, positions: List, timestamps: List) -> Dict:
    displacements = []
    directions = []
    speeds = []
    
    for i in range(1, len(positions)):
        pos1 = positions[i-1]
        pos2 = positions[i]
        time_diff = timestamps[i] - timestamps[i-1]
        
        displacement = np.linalg.norm(np.array(pos2) - np.array(pos1))
        displacements.append(displacement)
        
        direction = np.arctan2(pos2[1]-pos1[1], pos2[0]-pos1[0])
        directions.append(direction)
        
        speed = displacement / time_diff if time_diff > 0 else 0
        speeds.append(speed)
    
    return {
        'total_distance': sum(displacements),
        'average_speed': np.mean(speeds) if speeds else 0,
        'speed_variance': np.var(speeds) if speeds else 0,
        'direction_consistency': self._calculate_direction_consistency(directions),
        'trajectory_smoothness': self._calculate_trajectory_smoothness(positions)
    }

def predict_future_positions(self, 
                           current_state: Dict, 
                           history: List,
                           prediction_steps: int = 10) -> List:
    if len(history) < 5:
        return self._simple_extrapolation(current_state, prediction_steps)
    
    predictions = self._kalman_filter_prediction(current_state, history, prediction_steps)
    lstm_predictions = self._lstm_prediction(history, prediction_steps)
    fused_predictions = self._fuse_predictions(predictions, lstm_predictions)
    
    return fused_predictions

def _kalman_filter_prediction(self, current_state: Dict, history: List, steps: int) -> List:
    kf = cv2.KalmanFilter(4, 2)  # 4状态变量，2观测变量
    
    kf.transitionMatrix = np.array([
        [1, 0, 1, 0],
        [0, 1, 0, 1],
        [0, 0, 1, 0],
        [0, 0, 0, 1]
    ], np.float32)
    
    kf.measurementMatrix = np.array([
        [1, 0, 0, 0],
        [0, 1, 0, 0]
    ], np.float32)
    
    self._learn_kalman_parameters(kf, history)
    
    current_pos = current_state['position']
    current_vel = current_state.get('velocity', [0, 0])
    kf.statePost = np.array([current_pos[0], current_pos[1], 
                           current_vel[0], current_vel[1]], np.float32)
    
    predictions = []
    for _ in range(steps):
        prediction = kf.predict()
        predictions.append([prediction[0], prediction[1]])
    
    return predictions

class MultimodalFusionEngine:
    """多模态融合引擎"""
    
    def __init__(self, strategy: str = 'hybrid'):
        self.fusion_strategy = strategy
        self.fusion_methods = {
            'early': self._early_fusion,
            'late': self._late_fusion,
            'hybrid': self._hybrid_fusion
        }
        
        # 融合权重学习器
        self.weight_learner = FusionWeightLearner()
        
    async def fuse_modalities(self, multimodal_data: Dict) -> Dict:
        """多模态融合主方法"""
        if self.fusion_strategy not in self.fusion_methods:
            raise ValueError(f"不支持的融合策略: {self.fusion_strategy}")
        
        fusion_method = self.fusion_methods[self.fusion_strategy]
        return await fusion_method(multimodal_data)
    
    async def _early_fusion(self, data: Dict) -> Dict:
        """早期融合 - 数据级融合"""
        print("执行早期数据级融合...")
        
        fused_data = {}
        
        # 视觉和热成像数据融合
        if ModalityType.VISUAL in data and ModalityType.THERMAL in data:
            visual_data = data[ModalityType.VISUAL]
            thermal_data = data[ModalityType.THERMAL]
            
            # 对齐和配准
            aligned_thermal = self._align_thermal_to_visual(thermal_data, visual_data)
            
            # 多光谱融合
            fused_visual = self._multispectral_fusion(visual_data, aligned_thermal)
            fused_data['visual'] = fused_visual
        
        # 深度信息融合
        if ModalityType.DEPTH in data:
            depth_data = data[ModalityType.DEPTH]
            if 'visual' in fused_data:
                # 将深度信息与视觉特征结合
                fused_data['visual'] = self._fuse_depth_with_visual(
                    fused_data['visual'], depth_data
                )
            else:
                fused_data['depth'] = depth_data
        
        return fused_data
    
    async def _late_fusion(self, data: Dict) -> Dict:
        """晚期融合 - 决策级融合"""
        print("执行晚期决策级融合...")
        
        # 各模态独立检测
        modality_detections = {}
        
        for modality_type, modality_data in data.items():
            if modality_type == ModalityType.VISUAL:
                detections = await self._detect_with_visual(modality_data)
            elif modality_type == ModalityType.THERMAL:
                detections = await self._detect_with_thermal(modality_data)
            elif modality_type == ModalityType.DEPTH:
                detections = await self._detect_with_depth(modality_data)
            else:
                continue
                
            modality_detections[modality_type] = detections
        
        # 决策级融合
        fused_detections = self._fuse_detection_decisions(modality_detections)
        return {'fused_detections': fused_detections}
    
    async def _hybrid_fusion(self, data: Dict) -> Dict:
        """混合融合策略"""
        print("执行混合融合策略...")
        
        hybrid_results = {}
        
        # 1. 对关键区域进行早期融合
        roi_data = self._extract_regions_of_interest(data)
        early_fusion_results = await self._early_fusion(roi_data)
        
        # 2. 全局特征级融合
        feature_fusion_results = await self._feature_level_fusion(data)
        
        # 3. 决策级融合整合
        final_fusion = await self._late_fusion({
            **early_fusion_results,
            **feature_fusion_results
        })
        
        hybrid_results.update(final_fusion)
        hybrid_results['fusion_strategy'] = 'hybrid'
        hybrid_results['fusion_confidence'] = self._calculate_fusion_confidence(
            early_fusion_results, feature_fusion_results
        )
        
        return hybrid_results
    
    def _align_thermal_to_visual(self, thermal_data: np.ndarray, visual_data: np.ndarray) -> np.ndarray:
        """热成像与视觉图像对齐"""
        # 特征点检测和匹配
        visual_kp, visual_des = self._extract_features(visual_data)
        thermal_kp, thermal_des = self._extract_features(thermal_data)
        
        # 计算变换矩阵
        transformation = self._calculate_transformation(visual_kp, thermal_kp)
        
        # 应用变换
        aligned_thermal = cv2.warpPerspective(
            thermal_data, transformation, 
            (visual_data.shape[1], visual_data.shape[0])
        )
        
        return aligned_thermal
    
    def _multispectral_fusion(self, visual_img: np.ndarray, thermal_img: np.ndarray) -> np.ndarray:
        """多光谱图像融合"""
        # 将热成像图像转换为伪彩色
        thermal_colored = cv2.applyColorMap(
            cv2.normalize(thermal_img, None, 0, 255, cv2.NORM_MINMAX), 
            cv2.COLORMAP_JET
        )
        
        # 多分辨率融合
        fused_image = self._multiresolution_fusion(visual_img, thermal_colored)
        return fused_image
    
    def _multiresolution_fusion(self, img1: np.ndarray, img2: np.ndarray) -> np.ndarray:
        """多分辨率图像融合"""
        # 金字塔分解
        gaussian_pyramid1 = self._build_gaussian_pyramid(img1)
        gaussian_pyramid2 = self._build_gaussian_pyramid(img2)
        
        laplacian_pyramid1 = self._build_laplacian_pyramid(gaussian_pyramid1)
        laplacian_pyramid2 = self._build_laplacian_pyramid(gaussian_pyramid2)
        
        # 金字塔融合
        fused_pyramid = []
        for lap1, lap2 in zip(laplacian_pyramid1, laplacian_pyramid2):
            fused_level = (lap1 + lap2) / 2
            fused_pyramid.append(fused_level)
        
        # 金字塔重建
        fused_image = self._reconstruct_from_pyramid(fused_pyramid)
        return fused_image.astype(np.uint8)