Abstract
Mean shift tracking fails when the velocity of target is so large that the target’s window kernel in the previous frame can not cover the target in the current frame. Combination of mean shift and single Kalman filter also fails when the target’s velocity changed suddenly. To deal with the problem of tracking image target that has large and changing velocity, an efficient image tracking method integrated mean shift and double model filters is proposed. Two motion models can switch each other by using a probabilistic likelihood. Experiment results show the method integrated mean shift and double model filters can successfully keep tracking target, no matter the target’s velocity is large or small, changing or constant, with modest requirement of computation resource.
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Han, R., Jing, Z., Xiao, G. (2007). Probabilistic Motion Switch Tracking Method Based on Mean Shift and Double Model Filters. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_84
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DOI: https://doi.org/10.1007/978-3-540-72393-6_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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