Research And Implementation Of Multi-Scale Correlation Filters Target Tracking Algorithm

With the continuous development of high-tech such as 5G,big data and artificial intelligence,target tracking technology has become an important research hotspot in computer vision,providing effective help for subsequent target intelligent behavior analysis.It is widely used in security monitoring,military guidance,intelligence transportation and visual navigation.Correlation filters target tracking algorithm has strong stability and robustness,which is an important milestone of discriminant tracking algorithm in recent years.In the actual application environment,tracking algorithms are faced with the difficulties of scene diversity and complex changes of target scale.Multi-scale correlation filters target tracking algorithms can effectively improve tracking performance.The accuracy of target scale estimation has an important impact on tracking performance,but as the number of detection scales increases,its tracking time efficiency will seriously decrease,which has bad effect on real-time performance of multi-scale tracking algorithms.This paper uses two improved strategies to make full use of the feedback information of the tracking results for classification processing,and proposes a multi-scale correlation filtering tracking algorithm based on response discrimination.Improvement strategy one:Multi-resolution segmentation preprocessing method based on tracking target can effectively improve the tracking performance of low-resolution attribute video.Improvement strategy two:Multi-scale detection method based on response discrimination,through discriminant classification processing of the maximum response peak,multi-scale detection is not performed for video frames with more accurate target tracking,there by saving a lot of computing time.For video frames with in accurate target tracking performance,multi-scale detection is performed to correct the results and improve tracking performance.Comparing the current advanced multi-scale correlation filters target tracking algorithms on the OTB50,OTB100,TC128 and UAV123 data sets,the experimental results show that the improved algorithm in this paper can not only effectively increase the tracking speed and achieve real-time target tracking,but also significantly improve the tracking performance.In the OTB100 video library,the DP tracking accuracy average score of the improved algorithm in this paper is 4.4%,0.3%,7.1%,10.1%,and 10%higher than SAMF,SAMF_CA,fDSST,DSST,and KCF respectively;the average score of the AUC success rate is 2.7%,0.5%,2.5%,5.7%and 10.3%higher;the tracking speed of the improved algorithm in this paper is 1.65 times and 1.93 times of SAMF and SAMF_CA algorithms respectively.