使用OpenCV检测和追踪车辆

「爱情、让人受尽委屈。」 2022-09-28 13:04 170阅读 0赞

![20170311173838575][]

[完整源码GitHub  
][GitHub]

1.  使用高斯混合模型（BackgroundSubtractorMOG2）对背景建模，提取出前景
2.  使用中值滤波去掉椒盐噪声，再闭运算和开运算填充空洞
3.  使用cvBlob库追踪车辆，我稍微修改了cvBlob源码来通过编译

由于要对背景建模，这个方法要求背景是静止的

另外不同车辆白色区域不能连通，否则会认为是同一物体

void processVideo(char* videoFilename)
    {
    	Mat frame; // current frame
    	Mat fgMaskMOG2; // fg mask fg mask generated by MOG2 method
    	Mat bgImg; // background
    	Ptr<BackgroundSubtractorMOG2> pMOG2 = createBackgroundSubtractorMOG2(200, 36.0, false); // MOG2 Background subtractor
    
    	while (true)
    	{
    		VideoCapture capture(videoFilename);
    		if (!capture.isOpened())
    		{
    			cerr << "Unable to open video file: " << videoFilename << endl;
    			return;
    		}
    
    		int width = (int)capture.get(CV_CAP_PROP_FRAME_WIDTH);
    		int height = (int)capture.get(CV_CAP_PROP_FRAME_HEIGHT);
    
    		unique_ptr<IplImage, void(*)(IplImage*)> labelImg(cvCreateImage(cvSize(width, height), IPL_DEPTH_LABEL, 1), 
    			[](IplImage* p){ cvReleaseImage(&p); });
    		CvBlobs blobs;
    		CvTracks tracks;
    
    		while (true)
    		{
    			// read input data. ESC or 'q' for quitting
    			int key = waitKey(1);
    			if (key == 'q' || key == 27)
    				return;
    			if (!capture.read(frame))
    				break;
    
    			// update background
    			pMOG2->apply(frame, fgMaskMOG2);
    			pMOG2->getBackgroundImage(bgImg);
    			imshow("BG", bgImg);
    			imshow("Original mask", fgMaskMOG2);
    
    			// post process
    			medianBlur(fgMaskMOG2, fgMaskMOG2, 5);
    			imshow("medianBlur", fgMaskMOG2);
    			morphologyEx(fgMaskMOG2, fgMaskMOG2, MORPH_CLOSE, getStructuringElement(MORPH_RECT, Size(5, 5))); // fill black holes
    			morphologyEx(fgMaskMOG2, fgMaskMOG2, MORPH_OPEN, getStructuringElement(MORPH_RECT, Size(5, 5))); // fill white holes
    			imshow("morphologyEx", fgMaskMOG2);
    
    			// track
    			cvLabel(&IplImage(fgMaskMOG2), labelImg.get(), blobs);
    			cvFilterByArea(blobs, 64, 10000);
    			cvUpdateTracks(blobs, tracks, 10, 90, 30);
    			cvRenderTracks(tracks, &IplImage(frame), &IplImage(frame));
    
    			// show
    			imshow("Frame", frame);
    
    			key = waitKey(30);
    		}
    	}
    }

[20170311173838575]: /images/20220708/3184a76625b24080b583ad38d63feef6.png
[GitHub]: https://github.com/xfgryujk/VehicleDetection