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.
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.
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