智能监控摄像头行为异常检测算法深度解析：从理论到实践

# 代码1：物理引擎生成异常轨迹
import mujoco_py
import cv2

def generate_fall_trajectory():
    """模拟人体摔倒的骨骼轨迹"""
    model = mujoco_py.load_model_from_path="humanoid.xml"
    sim = mujoco_py.MjSim(model)
    # 在仿真中施加随机推力
    sim.data.qfrc_applied[2] = np.random.uniform(-500, -2000)
    trajectories = []
    for _ in range(150):  # 5秒@30fps
        sim.step()
        trajectories.append(sim.data.qpos[:3].copy())
    return np.array(trajectories)

# 将3D轨迹渲染为2D热图
def trajectory_to_heatmap(trajectory, camera_matrix):
    points_2d, _ = cv2.projectPoints(trajectory, ...)
    heatmap = np.zeros((1080, 1920))
    for x, y in points_2d:
        cv2.circle(heatmap, (int(x), int(y)), 15, 1, -1)
    return heatmap

graph TD
    A[摄像头RTSP流] -->|H.264| B(边缘盒子Nano)
    B -->|原始帧| C[轻量检测器]
    C -->|疑似异常| D[缓存队列]
    D -->|每5秒| E[云端重型模型]
    E -->|确认异常| F[钉钉告警]

# 代码2：实时异常行为检测
import torch
from norfair import Detection, Tracker
from scipy.spatial.distance import euclidean

class AnomalyDetector:
    def __init__(self):
        self.yolo = torch.hub.load('ultralytics/yolov5', 'yolov5s')
        self.tracker = Tracker(distance_function='euclidean', distance_threshold=800)
        self.speed_threshold = 50  # 像素/秒
        self.linger_threshold = 300  # 帧
        
    def detect(self, frame):
        # 1. 目标检测
        results = self.yolo(frame)
        detections = [Detection(p[:4], label=p[5]) for p in results.xyxy[0].cpu()]
        
        # 2. 多目标跟踪
        tracked_objects = self.tracker.update(detections)
        
        anomalies = []
        for obj in tracked_objects:
            # 3. 计算速度
            if len(obj.estimate) > 1:
                speed = euclidean(obj.estimate[-1], obj.estimate[-2])
                if speed > self.speed_threshold:
                    anomalies.append({
                        'type': '高速移动',
                        'track_id': obj.id,
                        'bbox': obj.estimate[-1]
                    })
            
            # 4. 徘徊检测
            if obj.age > self.linger_threshold:
                anomalies.append({
                    'type': '长时间滞留',
                    'track_id': obj.id,
                    'bbox': obj.estimate[-1]
                })
        return anomalies

# 代码3：基于骨骼点的异常检测
import torch
from torch import nn
from stgcn import ST_GCN_18

class SkeletonAnomalyNet(nn.Module):
    def __init__(self, in_channels=3, num_classes=10):
        super().__init__()
        self.stgcn = ST_GCN_18(in_channels, num_classes, 
                               graph_cfg={'layout': 'coco', 'strategy': 'uniform'})
        self.classifier = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(128, 2)  # 正常/异常
        )
        
    def forward(self, x):
        # x: [batch, channels, time, nodes, 2]
        x = self.stgcn(x)
        return self.classifier(x)

# 训练代码
def train_with_fake_normal():
    """仅用正常数据训练，通过重构误差判断异常"""
    model = SkeletonAnomalyNet()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    mse_loss = nn.MSELoss()
    
    for epoch in range(100):
        for normal_skeleton in dataloader:
            # 添加高斯噪声
            noisy = normal_skeleton + torch.randn_like(normal_skeleton) * 0.1
            reconstructed = model(noisy)
            loss = mse_loss(reconstructed, normal_skeleton)
            
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
    
    # 异常判断阈值
    threshold = np.percentile(losses, 95)  # 取5%分位数
    return model, threshold

# 代码4：知识蒸馏+量化
import torch.quantization as quant

class DistillationLoss(nn.Module):
    def __init__(self, temp=4.0):
        super().__init__()
        self.temp = temp
        self.ce = nn.KLDivLoss()
        
    def forward(self, student_logits, teacher_logits):
        soft_targets = torch.softmax(teacher_logits / self.temp, dim=1)
        return self.ce(torch.log_softmax(student_logits / self.temp, dim=1), 
                       soft_targets) * (self.temp ** 2)

# 量化配置
model.qconfig = quant.get_default_qconfig('fbgemm')
quantized_model = quant.prepare(model, inplace=False)
quantized_model = quant.convert(quantized_model, inplace=False)

# 最终模型大小：23MB（INT8），在Jetson Nano上推理速度从300ms降至45ms

# 代码5：横向联邦训练
import syft as sy

hook = sy.TorchHook(torch)
workers = [sy.VirtualWorker(hook, id=f"camera_{i}") for i in range(10)]

# 各摄像头本地训练
for round in range(50):
    local_models = []
    for worker in workers:
        model = local_train(worker.data)  # 数据不离开本地
        local_models.append(model.copy())
    
    # 联邦平均
    global_dict = {}
    for k in local_models[0].keys():
        global_dict[k] = torch.stack([model[k] for model in local_models]).mean(0)
    
    # 分发全局模型
    for worker in workers:
        worker.model.load_state_dict(global_dict)

# 代码6：生成对抗补丁
from torch.autograd import Variable

def generate_patch(frame, target_bbox):
    """在背包上生成对抗图案，使其被误检为‘安全帽’"""
    patch = Variable(torch.rand(3, 100, 100), requires_grad=True)
    optimizer = torch.optim.Adam([patch], lr=0.01)
    
    for _ in range(500):
        applied = apply_patch(frame, patch, target_bbox)
        pred = model(applied)
        loss = -torch.log(pred[0, '安全帽'])  # 最大化安全帽概率
        loss.backward()
        optimizer.step()
        patch.data.clamp_(0, 1)
    return patch

# 用DoWhy框架分析“员工摔倒”的真实原因
from dowhy import CausalModel

model = CausalModel(
    data=incident_df,
    treatment='地面湿滑',
    outcome='摔倒事件',
    common_causes=['照明条件', '鞋类', '年龄']
)
identified_estimand = model.identify_effect()
estimate = model.estimate_effect(identified_estimand, 
                                method_name="backdoor.linear_regression")
print(estimate)  # 地面湿滑导致摔倒的概率增加340%