Visual Object Tracking Based on Mean-shift and Particle-Kalman Filter

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Abstract

Even though many algorithms have been developed and many applications of object tracking have been made, object tracking is still considered as a difficult task to accomplish. The existence of several problems such as illumination variation, tracking non-rigid object, non-linear motion, occlusion, and requirement of real time implementation has made tracking as one of the challenging tasks in computer vision. In this paper a tracking algorithm which combines mean-shift and particle-Kalman filter is proposed to overcome above mentioned problems. The purpose of this combination is to draw each algorithm's strength points and cover each algorithms drawbacks. In the proposed method, mean-shift is used as master tracker when the target object is not occluded. When occlusion is occurred or the mean-shift tracking result is not convincing, particle-Kalman filter will act as master tracker to improve the tracking results. Experimental results of the proposed method show desirable performance in tracking objects under several above mentioned problems.

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APA

Iswanto, I. A., & Li, B. (2017). Visual Object Tracking Based on Mean-shift and Particle-Kalman Filter. In Procedia Computer Science (Vol. 116, pp. 587–595). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.10.010

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