Abstract
How to achieve a robust performance remains an intractable problem in the various object tracking algorithms due to some unfavorable factors, e.g. occlusions, appearance change, etc. In this paper, a robust object tracking approach is proposed based on a composite similarity measure. Experimental results on several challenging sequences demonstrate the effectiveness and feasibility of the proposed method.
The work was supported by a grant from National Natural Science Foundation of China (No. 61370109), a key project of support program for outstanding young talents of Anhui province university (No. gxyqZD2016013), a grant of science and technology program to strengthen police force (No. 1604d0802019), and a grant for academic and technical leaders and candidates of Anhui province (No. 2016H090).
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Ma, SH., Sun, ZL., Gu, CG. (2019). A Robust Object Tracking Approach with a Composite Similarity Measure. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM-2017. ELM 2017. Proceedings in Adaptation, Learning and Optimization, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-01520-6_26
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DOI: https://doi.org/10.1007/978-3-030-01520-6_26
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