这里写目录标题

1. 稀疏光流跟踪：Lucas-Kanade方法原理程序示例 2. 稠密光流跟踪：Farneback算法

1. 稀疏光流跟踪：Lucas-Kanade方法

原理

前提

亮度恒定。图像场景中目标的像素在帧间运动时外观上保持不变。对于灰度图像，需要假设像素被逐帧跟踪时且亮度不变。时间连续或者运动是“小运动”。图像的运动随时间的变化比较缓慢。实际应用中指的是时间变化相对图像中运动的比例要足够小,这样目标在帧间的运动就比较小。空间一致。一个场景中同一表面上邻近的点具有相似的运动，在图像平面上的投影也在邻近区域。 void calcOpticalFlowPyrLK(InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, Size winSize = Size(21, 21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4 )

参数详解：

第一个参数：深度为8位的前一帧图像或金字塔图像。第二个参数：和prevImg有向图第三个参数：计算光流所需要的输入2D点矢量。点坐标必须时单精度浮点数。第四个参数：输出2D矢量。第五个参数：输出状态矢量。第六个参数：输出误差矢量。第七个参数：每个金字塔层搜索窗口大小。第八个参数：金字塔层的最大数目。第九个参数：指定搜索算法收敛迭代的类型。第十个参数：算法计算的光流等式的2x2常规矩阵的最小特征值。

程序示例

int main(int argc, char* argv[]) { // Variable declaration and initialization // Iterate until the user hits the Esc key while(true) { // Capture the current frame cap >> frame; // Check if the frame is empty if(frame.empty()) break; //Resize the frame resize(frame, frame, Size(), scalingFactor, scalingFactor, INTER_AREA); // Copy the input frame frame.copyTo(image); // Convert the image ot gtayscale cvtColor(image, curGrayImage, COLOR_BGR2GRAY); // Check if there are points to track if(!trackingPoints[0].empty()) { // Status vector to indicate whether the flow for the corresponding features has been found vector<uchar> statusVector; // Error vector to indicate the error for the corresponding feature vector<float> errorVector; // Check if previous image is empty if(prevGrayImage.empty()) { curGrayImage.copyTo(preGrayImage); } // Calculate the optial flow using Lucas-Kanade algorithm calcOpticalFlowPyrLK(prevGrayImage, curGrayImage, trackingPoints[0], trackingPoints[1], statusVector, errorVector, windowSize, 3, terminationCriteria, 0, 0.001); int count = 0; // Minimum distace between any two tracking points int minDist = 7; for(int i = 0; i < trackingPoints[1].size(); i++) { if(pointTrackingFlag) { // If the new point is within 'minDist' distance from an existing point, it will not be tracked if(norm(currentPoint - trackingPoints[1][i]) <= minDist) { pointTrackingFlag = false; continue; } } // Check if the status vector is good if(!statusVector[i]) continue; trackingPoints[1][count ++] = trackingPoints[1][i]; // Draw a filled circle for each of the tracking points int radius = 8; int thickness = 2; int lineType = 8; circle(image, trackingPoints[1][i], radius, Scalar(0, 255, 0), thickness, lineType); } trackingPoints[1].resize(count); } // Refining the location of the feature points if(pointTrackingFlag && trackingPoints[1].size() < maxNumPoints) { vector<Pointt2f> tempPoints; // Function to refine the location of the corners to subpixel accuracy // Here 'pixel' refers to the image path of size 'windowSize' and not the actual image pixel cornerSubPix(curGrayImage, tempPoints, windowSize, Size(-1, -1), terminationCriteria); trackingPoints[1].push_back(tempPoints[0]); pointTrackingFlag = false; } // Display the image with the tracking points imshow(windowName, image); // Check if the user pressed the Esc key char ch = waitkey(10); if(ch == 27) break; // Swap the 'points' vectors to update 'previous' to 'current' std::swap(trackingPoints[1], trackingPoints[0]); // Swap the images to update previous image to current image cv::swap(prevGrayImage, curGrayImage); } return 1; }

2. 稠密光流跟踪：Farneback算法