Abstract
In this paper, we propose an advanced mean-shift tracking based on multi-mode kernel considering the background weight in which the kernel is divided into multiple sub-kernels in order to detect the partial or full occlusion. The proposed method includes occlusion detection based on coefficient of variance of Bhattacharya coefficients for multi-mode kernels. Experimental results show that the proposed method is able to robustly track a target object with partial and long term full occlusion in moving camera environment.
Keywords
- Visual object tracking
- Kernel-based tracking
- Multi-model kernel tracking
- Occlusion handling
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© 2015 Springer Science+Business Media Singapore
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Kim, ES., Hong, M., Choi, YJ. (2015). Occlusion Detection Using Multi-Mode Mean-Shift Tracking. In: Park, DS., Chao, HC., Jeong, YS., Park, J. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-10-0281-6_85
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DOI: https://doi.org/10.1007/978-981-10-0281-6_85
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Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0280-9
Online ISBN: 978-981-10-0281-6
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