Abstract
The traditional MeanShift algorithm cannot obtain accurate tracking results in some complex situations where tracking targets have scale changes or similar color with background. In this paper, a new MeanShift target tracking algorithm, namely DEPTH & SIFT-MeanShift algorithm, is proposed by using a depth camera and SIFT (Scale Invariant Feature Transform) feature metric. The algorithm firstly combines feature points extracted from gray and depth images respectively, and then represents tracked objects with Modulus, i.e. Direction distribution histogram of feature points in the tracking object field, so that targets can be effectively tracked. Experimental results show that the proposed algorithm can achieve good tracking performance when the tracking target changes its scale, and have the strong adaptability to occlusion. Moreover, it is very robust to illumination changes, and able to discriminate targets from background very well.
This work is supported by Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 14JC1402200.
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Lu, L., Fei, M., Wang, H., Hu, H. (2017). A New Meanshift Target Tracking Algorithm by Combining Feature Points from Gray and Depth Images. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_54
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DOI: https://doi.org/10.1007/978-981-10-6370-1_54
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