使用MOG2对运动物体进行检测

本篇内容主要是对运动物体进行检测，通过使用MOG2（高斯混合模型建模）来进行物体运动前背景分离，来检测运动的物体，最后对其进行图像处理，再画出其最小外接矩形，达到对运动物体的分割与检测。具体代码如下：

#include<opencv.hpp>
#include<imgproc.hpp>
#include<highgui.hpp>
#include<cstdio>
#include<vector>
#include<iostream>
#include<opencv2/video/background_segm.hpp>

using namespace std;
using namespace cv;

int main()
{
	// Current frame
	Mat frame;
	// Foreground mask generated by MOG2 method
	Mat fgMaskMOG2;
	// Background
	Mat bgImg;
	// MOG2 Background subtractor
	Ptr<BackgroundSubtractorMOG2> pMOG2 = createBackgroundSubtractorMOG2(200, 36.0, false);

	while (true)
	{
		VideoCapture capture("D:\\MOG2detect.mp4");
		if (!capture.isOpened())
		{
			cerr << "Unable to open video file: " << endl;
			return -1;
		}

		int width = (int)capture.get(CAP_PROP_FRAME_WIDTH);
		int height = (int)capture.get(CAP_PROP_FRAME_HEIGHT);

		while (true)
		{
			Point s, p;
			// Read input data. Press ESC or Q to quit
			int key = waitKey(1);
			if (key == 'q' || key == 27)
				return 0;
			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);
			// Fill black holes
			morphologyEx(fgMaskMOG2, fgMaskMOG2, MORPH_CLOSE, getStructuringElement(MORPH_RECT, Size(12, 12)));
			// Fill white holes
			morphologyEx(fgMaskMOG2, fgMaskMOG2, MORPH_OPEN, getStructuringElement(MORPH_RECT, Size(3, 3)));
			imshow("morphologyEx", fgMaskMOG2);
			
			vector<vector<Point> > contours;
			vector<Vec4i> hierarchy;
			findContours(fgMaskMOG2, contours, hierarchy, RETR_CCOMP , CHAIN_APPROX_NONE);	//find the contours

			//Find the minimum enclosing rectangle
			vector<Rect> boundRect(contours.size());  //Define the set of enclosing rectangles
			vector<RotatedRect> box(contours.size()); //Define the minimum set of enclosing rectangles
			Point2f rect[4];

			for (int i = 0; i < contours.size(); i++)
			{
				box[i] = minAreaRect(Mat(contours[i]));  //Calculate the minimum enclosing rectangle for each contour
				boundRect[i] = boundingRect(Mat(contours[i]));
				//circle(frame, Point(box[i].center.x, box[i].center.y), 5, Scalar(0, 255, 0), -1, 8);  //Draw center point for the minumum enclosing rectangles
				box[i].points(rect);  //Assign the four ends of the smallest enclosing rectangle to the rect array
				//rectangle(frame, Point(boundRect[i].x, boundRect[i].y), Point(boundRect[i].x + boundRect[i].width, boundRect[i].y + boundRect[i].height), Scalar(0, 255, 0), 2, 8);

				circle(frame, s, 5, Scalar(0, 255, 0), -1, 8);
				for (int j = 0; j < 4; j++)
				{
					line(frame, rect[j], rect[(j + 1) % 4], Scalar(0, 0, 255), 2, 8);  //Rraw the edge of the smallest enclosing rectangle
				}
			}

			// Show the frame
			imshow("Frame", frame);
			key = waitKey(30);

		}
	}
}

检测效果不是很好，但是可以参考卡尔曼滤波器进行预测可以使检测效果更平滑，不会出现突变情况。接下来是检测效果：

有兴趣的可以加入卡尔曼滤波进行平滑处理，效果会更好。如果有不好的地方欢迎大家批评指正。