Abstract
The Mean Shift algorithm for tracking the location of an object has recently gained considerable interest because of its speediness and efficiency. However, the appearance description using only color features cannot provide enough information when the target and its background have similar colors. In response to this problem, an improved human tracking system based on Mean Shift algorithm is incorporated in this paper. The proposed method combines color-texture features and background information to find the most distinguished features between the target and background for target representation. The experimental results show that the proposed method presents a good compromise between computational cost and accuracy; its performance is compared with recent state-of-the-art algorithm on Benchmark dataset and it achieved excellent results.
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References
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A.R., Van den Hengel, A.: A survey of appearance models in visual object tracking. ACM Trans. Intel. Syst. Technol. 4(4), Article 58 (2013)
Laaroussi, K., Saaidi, A., Masrar, M., Satori, K.: Video-surveillance system for tracking moving people using color interest points. World Appl Sci J 32(2), 289–301 (2014)
Laaroussi, K., Saaidi, A., Satori, K.: People tracking using color control points and skin color. J. Emerg. Technol Web Intell 6(1), 94–100 (2014)
He, L., Xu, Y., Chen, Y., Wen, J.: Recent advance on mean shift tracking: A survey. Int. J. Image Graph. 13(3) (2013)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Ojala, T., Pietikäinen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Patt. Anal. Mach. Intell. 24(7), 971–987 (2002)
Qingchang, G., Xiaojuan, C., Hongxia, C.: Mean-shift of variable window based on the Epanechnikov Kernel. In: Proceedings of the International Conference on Mechatronics and Automation, pp. 2314–2319 (2007)
Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Patt. Anal. Mach. Intell. 28(4), 657–662 (2006)
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Laaroussi, K., Saaidi, A., Masrar, M., Satori, K. (2016). Human Tracking Based on Appearance Model. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol 380. Springer, Cham. https://doi.org/10.1007/978-3-319-30301-7_31
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DOI: https://doi.org/10.1007/978-3-319-30301-7_31
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