Research On Improved KCF Object Tracking Algorithms

With growing of artificial intelligence,computer vision has been rapid development in recent years.Visual target tracking is one of the most important part of computer vision research,with widely used in many fields.The application situation is becoming more and more complicated due to the wide application fields.The existence of scale changes,occlusion,rotation,complex background,low resolution and other factors has brought higher challenges to visual target tracking.KCF(Kernelized Correlation Filters)tracking algorithm has been widely concerned since it was proposed.Based on the KCF tracking algorithm,this paper proposed improved methods for the shortcoming of KCF tracking algorithm.Firstly,the SMIKCF(Scale-Adaptive Multiple-Feature Improved-Template-Update KCF)tracking algorithm is proposed in this paper to solve the problem that the KCF tracking algorithm can not adapt to the target scale.On the basis of KCF algorithm,SMIKCF adds a scale estimation filter,and combines HOG characteristics and CN characteristics,using the APCE criterion to improve the updating method of the position estimation filter model.Secondly,the AOKCF(Anti-Occlusion KCF)tracking algorithm is proposed to solve the problem of occlusion.AOKCF tracking algorithm is based on KCF tracking algorithm.APCE criterion is used to check the reliability of tracking results.When the tracking result is unreliable,add detection module to detect the target,and then use the position filter to the target recognition.If the target is recognized,then update the target position and the position filter module.Otherwise,go directly to the next frame.The experimental datas are from Visual Tracker Benchmark.Experiments were performed on Benchmark's 50 test video sequences and use the OPE(One-Pass Evaluation)evaluation method to evaluate the performance of the algorithm.Experimental results show that the proposed method can effectively solve the related problems and improve the performance of the tracking algorithm.