Abstract
In recent years, one of the popular swarm intelligence algorithm Particle Swarm Optimization has demonstrated to have efficient and accurate outcomes for tracking different object movement. But there are still problems of multiple interferences in object tracking need to overcome. In this paper, we propose a new multiple swarm approach to improve the efficiency of the particle swarm optimization in object tracking. This proposed algorithm will allocate multiple swarms in separate frame grids to provide higher accuracy and wider search domain to overcome some interferences problem which can produce a stable and precise tracking orbit. It can also achieve better quality in target focusing and retrieval. The results in real environment experiments have been proved to have better performance when compare to other traditional methods like Particle Filter, Genetic Algorithm and traditional PSO.
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Sha, F., Yeung, H.W.F., Chung, Y.Y., Liu, G., Yeh, WC. (2016). Multi-swarm Particle Grid Optimization for Object Tracking. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_79
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DOI: https://doi.org/10.1007/978-3-319-46672-9_79
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