The New Kernel-based Multiple Instances Learning Algorithm for Object Tracking

Abstract

Background:: Visual tracking is a crucial component of computer vision systems. Objective:: To deal with the problems of occlusion, pose variation, and illumination in long-time tracking, we propose a new kernel-based multiple instances learning tracker. objective: visual tracking Method:: The tracker captures five positive bags, including the occlusion bag, pose bag, illumination bag, scale bag, and object bag, to deal with the appearance changes of an object in a complex environment. A Gaussian kernel function is used to compute the inner product for selecting the powerful weak classifiers, which further improves the efficiency of the tracker. Moreover, the tracking situation is determined by using these five classifiers, and the correlating classifiers are updated. Results:: The experimental results show that the proposed algorithm is robust in terms of occlusion and various appearance changes. Conclusion:: The proposed algorithm preforms well in complex situations. conclusion: And it preforms well in complex situation.