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基于OpenCV C++的行人检测与人流量统计算法

news 2025/11/14 13:23:43

基于OpenCV C++的行人检测与人流量统计算法，整合了背景减除、目标跟踪和区域计数技术：

一、核心实现框架

#include <opencv2/opencv.hpp>
#include <vector>
#include <map>using namespace cv;
using namespace std;// 定义行人结构体
struct Pedestrian {int id;Rect boundingBox;Point2f center;bool isCounted = false;
};// 全局变量
vector<Pedestrian> pedestrians;
map<int, Rect> trackers;
int countIn = 0, countOut = 0;
Rect entrance(0, 0, 200, 480);  // 入口区域
Rect exit(600, 0, 200, 480);    // 出口区域

二、行人检测模块

1. HOG+SVM检测器初始化

Ptr<HOGDescriptor> hog = HOGDescriptor();
hog->setSVMDetector(HOGDescriptor::getDefaultPeopleDetector());

2. 检测函数实现

vector<Rect> detectPedestrians(Mat &frame) {vector<Rect> detections;hog->detectMultiScale(frame, detections, 0, Size(8,8), Size(32,32), 1.05, 2);// 非极大值抑制vector<int> indices;NMSBoxes(detections, confidences, 0.5, 0.4, indices);vector<Rect> filtered;for(auto i : indices) filtered.push_back(detections[i]);return filtered;
}

三、目标跟踪模块

1. 卡尔曼滤波跟踪器

vector<KalmanFilter> kalmanFilters;void initTrackers(const vector<Rect> &detections) {for(auto &det : detections) {KalmanFilter kf(4, 2, 0);kf.transitionMatrix = (Mat_<float>(4,4) << 1,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1);kf.processNoiseCov = (Mat_<float>(4,4) << 1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1)*1e-2;kf.measurementMatrix = (Mat_<float>(2,4) << 1,0,0,0,0,1,0,0);kf.statePost.at<float>(0) = det.x + det.width/2;kf.statePost.at<float>(1) = det.y + det.height/2;kalmanFilters.push_back(kf);}
}

2. 跟踪更新逻辑

void updateTracking(Mat &frame) {for(size_t i=0; i<trackers.size(); i++) {Rect &rect = trackers[i];KalmanFilter &kf = kalmanFilters[i];// 预测Mat prediction = kf.predict();// 更新Point center(rect.x + rect.width/2, rect.y + rect.height/2);Mat measured = (Mat_<float>(2,1) << center.x, center.y);kf.correct(measured);// 更新边界框rect.x = max(0, (int)(prediction.at<float>(0) - rect.width/2));rect.y = max(0, (int)(prediction.at<float>(1) - rect.height/2));rect.width = min(frame.cols - rect.x, 50);rect.height = min(frame.rows - rect.y, 100);}
}

四、人流量统计算法

1. 区域检测函数

bool isCrossing(const Point2f &center) {// 入口检测if(center.x >= entrance.x && center.x <= entrance.x + entrance.width &&center.y >= entrance.y && center.y <= entrance.y + entrance.height) {return true;}// 出口检测if(center.x >= exit.x && center.x <= exit.x + exit.width &&center.y >= exit.y && center.y <= exit.y + exit.height) {return true;}return false;
}

2. 计数逻辑

void countPeople(Mat &frame) {for(auto &ped : pedestrians) {Point2f center = ped.center;// 新增检测if(!ped.isCounted && isCrossing(center)) {ped.isCounted = true;if(insideRegion(center, entrance)) countIn++;else if(insideRegion(center, exit)) countOut++;}// 绘制跟踪信息rectangle(frame, ped.boundingBox, Scalar(0,255,0), 2);putText(frame, to_string(ped.id), ped.boundingBox.tl(), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,255));}
}

五、主程序流程

int main() {VideoCapture cap("input.mp4");Mat frame, gray;// 初始化检测器和跟踪器Ptr<BackgroundSubtractorMOG2> pMOG2 = createBackgroundSubtractorMOG2();initTrackers(detectPedestrians(frame));while(true) {cap >> frame;if(frame.empty()) break;// 背景减除pMOG2->apply(frame, gray);threshold(gray, gray, 128, 255, THRESH_BINARY);// 检测行人vector<Rect> detections = detectPedestrians(frame);// 更新跟踪器updateTracking(frame);// 人流量统计countPeople(frame);// 显示结果imshow("Pedestrian Counting", frame);if(waitKey(30) == 27) break;}return 0;
}

参考代码 OPENCV_行人流量检测 www.youwenfan.com/contentcsl/60272.html

六、优化

多尺度检测：采用图像金字塔处理不同大小行人
数据关联：使用匈牙利算法进行检测框与跟踪器的匹配
遮挡处理：设置最大丢失帧数（如5帧），超时后重新初始化跟踪器
性能优化：
- 使用积分图像加速HOG特征计算
- 采用并行计算处理多行人跟踪
- 降低检测频率（如每3帧检测一次）

七、调试与验证

1. 参数调优建议

参数	推荐值	作用
检测尺度	8,8	平衡检测精度与速度
非极大抑制阈值	0.4	减少重叠检测框
跟踪器更新间隔	30ms	控制跟踪延迟
区域检测灵敏度	0.8	防止误触发计数