Research On Video Target Tracking Algorithm Based On Kernelized Correlation Filters

As a research hotspot of computer vision,video target tracking is wi'dely used in various fields,such as people's livelihood and military.The Kernelized Correlation Filters(KCF)object tracking algorithm uses the HOG feature,which performance is better than the other features to improve the tracking accuracy.And it performs an approximate dense sampling method by cyclic shift.Then it uses the kernel mechanism to realize the faster calculation of the inner product in high dimensional space,which reduces the computational complexity and obtains a higher tracking speed.Due to the diversity of the actual tracking environment and the complex changes of tracking scenes and targets,the KCF algorithm has poor video tracking performance for fast motion,motion blur,out-of-view,scale change and rigid deformation.Fast motion video scenes tend to cause drift of the target tracking frame,and target tracking errors may continue to spread and accumulate,eventually causing serious failure of the target tracking.In order to solve the above problems,this paper proposes an improved Adaptive Multi-sampling KCF target tracking algoritlun.The algorithm introduces the PSNR tracking error judgment mechanism to judge whether the current target tracking result is correct,and uses the multi-sample blocks target tracking detection method to improve the tracking accuracy.If the current target position is tracked correctly,the tracking target is output;otherwise,if the current target position tracking error is judged,the KCF algorithm is used to detect multiple sampling blocks,where in the position of the sampling blocks filter responses'maximum value is the final tracked target position.The algorithm involves tracking error discriminant indicator threshold T,multi-sampling blocks distribution offset step size S,and multi-sampling blocks distribution method M·Through a large number of experimental tests and analysis,the optimal selection of parameters are given in this paper.Compared with the current advanced correlation filtering target tracking algorithm,the experimental results show that the improved algorithm has a better tracking performance.Based on the KCF algorithm,the accuracy and success rate of the improved algorithm are increased by 5.3%and 4.3%,respectively.Especially for the video of fast motion,motion blur,and out-of-view,the algorithm of this paper improves the accuracy by 17.6%,22%and 28.2%respectively.And the average tracking rate reaches 108.75 FPS,which proves that the algorithm has a better tracking accuracy based on high rate tracking.