Abstract: Visual tracking is an important part of the computer vision,and kernelized correlation filter tracking is a relatively novel method in visual tracking field.It is different from traditional method based on target feature,which has high accuracy and fast tracking speed.However,when the object moves rapidly or has the larger scale changes,the method cannot track the target accurately.This paper proposed an improved algorithm based on the correlative filter which can effectively overcome the above problems.The learning factors of kernelized correlation filtering and the ada-ptive updating model of learning factors are determined by using random update multi-template matching.Experimental results show that the algorithm can adjust the learning factors quickly according to different scenarios,thus the success rate of tracking will be improved.Through adaptive learning factor and multi-template matching,this algorithm has robust adaptability to partial occlusion,illumination and target scale.

Key words: Kernelized correlation filtering, Multi-template matching, Random update, Target tracking