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Video Object Tracking with Heuristic Optimization Methods

Chin-Shiuh Shieh, Yong-Shixa Jhan, Yuan-Li Liu, Mong-Fong Horng, and Tsair-Fwu Lee
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, R.O.C.

Abstract—Object tracking is a common and essential task in video processing. This study approaches the object tracking problem using heuristic optimization methods. HSV color space is used as features for object matching. We evaluate the performance of particle filter, particle swarm optimization and grey wolf optimizer. Tracking rate, tracking accuracy and tracking time are important criteria in our comparative study. Experimental results reveal that particle swarm optimization prevails in object tracking applications.

Index Terms—HSV color space, particle filter, particle swarm optimization, grey wolf optimizer

Cite: Chin-Shiuh Shieh, Yong-Shixa Jhan, Yuan-Li Liu, Mong-Fong Horng, and Tsair-Fwu Lee, "Video Object Tracking with Heuristic Optimization Methods," Journal of Image and Graphics, Vol. 6, No. 2, pp. 95-99, December 2018. doi: 10.18178/joig.6.2.95-99

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