feat: 新增人员徘徊/静止行为分析功能

本次提交实现了完整的人员行为分析系统，包括： 1. 新增基于位置和跟踪ID的两种行为检测算法 2. 新增徘徊检测服务与行为处理器模块 3. 前后端集成算法配置界面与告警展示 4. 支持图片和视频流场景下的行为分析 5. 新增算法配置接口与文档说明具体改动： - 新增loitering_detection模型目录与算法实现 - 新增AlgorithmConfig组件实现可视化配置 - 扩展图片/视频检测接口支持算法参数传递 - 新增行为告警推送与前端展示页面 - 优化检测服务，集成行为分析逻辑 - 移除冗余日志输出，完善代码注释
2026-05-19 09:17:09 +08:00
parent 2691761f01
commit 7aa71c5f83
15 changed files with 1937 additions and 76 deletions
--- a/models/loitering_detection/algorithms/stationary_detector.py
+++ b/models/loitering_detection/algorithms/stationary_detector.py
@@ -0,0 +1,236 @@
+"""
+基于位置的静止人员检测算法
+不依赖跟踪 ID，而是根据位置来关联人员
+适用于跟踪不稳定但人员相对静止的场景
+"""
+
+import time
+from typing import Dict, List, Tuple, Optional
+from dataclasses import dataclass, field
+from collections import defaultdict
+
+
+@dataclass
+class PositionRecord:
+    """位置记录"""
+    first_seen: float
+    last_seen: float
+    center: Tuple[int, int]
+    box: Tuple[int, int, int, int]
+    duration: float = 0.0
+
+
+class PositionBasedStationaryDetector:
+    """
+    基于位置的静止检测器
+    
+    特点：
+    - 不依赖跟踪 ID，直接用位置关联人员
+    - 适用于 SORT 等跟踪器不稳定的场景
+    - 使用网格化位置 + 距离容差进行匹配
+    """
+    
+    def __init__(
+        self,
+        stationary_threshold: float = 10.0,  # 静止阈值（秒）
+        position_tolerance: int = 50,         # 位置容差（像素）
+        cleanup_interval: float = 5.0         # 清理间隔（秒）
+    ):
+        self.stationary_threshold = stationary_threshold
+        self.position_tolerance = position_tolerance
+        self.cleanup_interval = cleanup_interval
+        
+        # 位置历史记录: {position_key: PositionRecord}
+        self._position_history: Dict[Tuple[int, int], PositionRecord] = {}
+        self._last_cleanup = time.time()
+    
+    def _get_position_key(self, center: Tuple[int, int]) -> Tuple[int, int]:
+        """
+        将连续坐标转换为离散的位置键
+        用于将相近位置归为一类
+        """
+        x, y = center
+        grid_x = int(x / self.position_tolerance)
+        grid_y = int(y / self.position_tolerance)
+        return (grid_x, grid_y)
+    
+    def _find_matching_position(
+        self, 
+        center: Tuple[int, int]
+    ) -> Optional[Tuple[int, int]]:
+        """
+        查找与当前位置匹配的历史位置
+        返回匹配的位置键，如果没有则返回 None
+        """
+        current_key = self._get_position_key(center)
+        
+        # 首先检查精确匹配
+        if current_key in self._position_history:
+            hist_center = self._position_history[current_key].center
+            distance = ((center[0] - hist_center[0]) ** 2 + 
+                       (center[1] - hist_center[1]) ** 2) ** 0.5
+            if distance < self.position_tolerance:
+                return current_key
+        
+        # 检查相邻网格
+        for dx in [-1, 0, 1]:
+            for dy in [-1, 0, 1]:
+                if dx == 0 and dy == 0:
+                    continue
+                neighbor_key = (current_key[0] + dx, current_key[1] + dy)
+                if neighbor_key in self._position_history:
+                    hist_center = self._position_history[neighbor_key].center
+                    distance = ((center[0] - hist_center[0]) ** 2 + 
+                               (center[1] - hist_center[1]) ** 2) ** 0.5
+                    if distance < self.position_tolerance:
+                        return neighbor_key
+        
+        return None
+    
+    def update(
+        self,
+        center: Tuple[int, int],
+        box: Tuple[int, int, int, int]
+    ) -> Tuple[str, float, bool]:
+        """
+        更新位置信息
+        
+        Args:
+            center: (x, y) 中心点坐标
+            box: (x1, y1, x2, y2) 边界框
+        
+        Returns:
+            position_id: 位置 ID（用于关联）
+            stationary_duration: 静止时长（秒）
+            is_stationary: 是否静止超过阈值
+        """
+        current_time = time.time()
+        
+        # 定期清理旧记录
+        if current_time - self._last_cleanup > self.cleanup_interval:
+            self.cleanup_old_positions()
+            self._last_cleanup = current_time
+        
+        # 查找匹配的历史位置
+        matching_key = self._find_matching_position(center)
+        
+        if matching_key is not None:
+            # 更新已有位置
+            record = self._position_history[matching_key]
+            record.last_seen = current_time
+            
+            # 平滑更新中心位置（使用移动平均）
+            old_center = record.center
+            record.center = (
+                int(0.7 * old_center[0] + 0.3 * center[0]),
+                int(0.7 * old_center[1] + 0.3 * center[1])
+            )
+            record.box = box
+            
+            duration = current_time - record.first_seen
+            record.duration = duration
+            
+            is_stationary = duration > self.stationary_threshold
+            position_id = f"pos_{matching_key[0]}_{matching_key[1]}"
+            
+            return position_id, duration, is_stationary
+        else:
+            # 创建新位置记录
+            new_key = self._get_position_key(center)
+            self._position_history[new_key] = PositionRecord(
+                first_seen=current_time,
+                last_seen=current_time,
+                center=center,
+                box=box,
+                duration=0.0
+            )
+            new_id = f"pos_{new_key[0]}_{new_key[1]}"
+            return new_id, 0.0, False
+    
+    def cleanup_old_positions(self, max_age: float = 5.0) -> int:
+        """
+        清理长时间未更新的位置记录
+        
+        Args:
+            max_age: 最大保留时间（秒）
+        
+        Returns:
+            清理的记录数量
+        """
+        current_time = time.time()
+        to_remove = [
+            key for key, data in self._position_history.items()
+            if current_time - data.last_seen > max_age
+        ]
+        
+        for key in to_remove:
+            del self._position_history[key]
+        
+        return len(to_remove)
+    
+    def get_all_stationary(
+        self, 
+        threshold: Optional[float] = None
+    ) -> List[Dict]:
+        """
+        获取所有静止超过阈值的位置
+        
+        Args:
+            threshold: 静止阈值（秒），默认使用初始化时的阈值
+        
+        Returns:
+            list: [{position_id, duration, center, box}, ...]
+        """
+        threshold = threshold or self.stationary_threshold
+        
+        result = []
+        for key, data in self._position_history.items():
+            if data.duration > threshold:
+                result.append({
+                    'position_id': f"pos_{key[0]}_{key[1]}",
+                    'duration': data.duration,
+                    'center': data.center,
+                    'box': data.box
+                })
+        
+        # 按时长排序
+        result.sort(key=lambda x: x['duration'], reverse=True)
+        return result
+    
+    def reset(self):
+        """重置所有跟踪数据"""
+        self._position_history.clear()
+        self._last_cleanup = time.time()
+    
+    def detect(
+        self,
+        detections: List[Dict]
+    ) -> List[Dict]:
+        """
+        批量检测静止状态
+        
+        Args:
+            detections: 检测结果列表，每项包含 'bbox': [x1, y1, x2, y2]
+        
+        Returns:
+            添加 'stationary_info' 字段的检测结果
+        """
+        results = []
+        
+        for det in detections:
+            x1, y1, x2, y2 = det['bbox']
+            center = ((x1 + x2) // 2, (y1 + y2) // 2)
+            box = (x1, y1, x2, y2)
+            
+            position_id, duration, is_stationary = self.update(center, box)
+            
+            det_copy = det.copy()
+            det_copy['stationary_info'] = {
+                'position_id': position_id,
+                'duration': round(duration, 2),
+                'is_stationary': is_stationary,
+                'threshold': self.stationary_threshold
+            }
+            results.append(det_copy)
+        
+        return results