Moving Object Tracking Based on Camshift Algorithm
DOI:
https://doi.org/10.14738/aivp.74.6702Keywords:
Camshift; target tracking; Kalman; adaptive; mean-shift.Abstract
Continuously adaptive Camshift is an efficient and lightweight tracking algorithm developed based on mean-shift. Camshift algorithm has the advantage of better real-time, but this algorithm is only suitable for tracking targets in simple cases, not well for tracking desired targets in complex situation. In this paper, we will present an improved method of multiple targets tracking algorithm based on the Camshift algorithm combined with Kalman filter. The tracker of the improved method was used to track each detected target. It can achieve tracking of multiple targets. A large number of experiments have proved that this algorithm has strong target recognition ability, good anti-noise performance, and fast-tracking speed.
References
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[2]. Wang Zhaowen, Yang Xiaokang, Xu Yi, et al., “CamShift guided particle filter for visual tracking,” Pattern Recognition Letters, v 30, n 4, pp.407-413, 2009.
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[4]. Shen Xuanjing, and Zhang Bo, “CamShift tracker based on image moments,” Journal of Beijing University of Technology, v 38, n 1, p 105-109, 2012.
[5]. W. Xiangyu, and L. Xiujuan, "The study of moving target tracking based on Kalman-CAMShift in the video," 2nd International Conference on Information Science and Engineering (ICISE), pp.1-4, 2010.
[6]. J. Yin, Y. Han, J. Li, and A. Cao, "Research on Real-Time Object Tracking by Improved CAMShift," International Symposium on Computer Network and Multimedia Technology, pp.1-4, 2009.
[7]. G. J. Allen, Y. D. Richard Xu and S. Jin Jesse, “Object Tracking Using CAMShift Algorithm and Multiple Quantized Feature Spaces”, Inc. Australian Computer Society, vol.36, 2004.
[8]. Li Chao, Liu Tiegen, Liu Hongli, et al., “Face tracking based on Haar detection and improved Camshift algorithm,” Journal of Optoelectronics Laser, v 22, n 12, p 1852-1856, 2011.
[9]. Comaniciu Dorin, Ramesh Visvanathan, and Meer Peter, “Kernel-based object tracking,” IEEE Trans. on Pattern Analysis and Machine Intelligence, v25, n5, pp.564-577, 2003.
[2]. Wang Zhaowen, Yang Xiaokang, Xu Yi, et al., “CamShift guided particle filter for visual tracking,” Pattern Recognition Letters, v 30, n 4, pp.407-413, 2009.
[3]. Sun Hongguang,Zhang Jin,Liu Yantao,et al., “Optimized Particle Filter Tracking by CamShift Based on Multi-feature,” Opto-Electronic Engineering, v37, n2, pp 1-6,31,2010.
[4]. Shen Xuanjing, and Zhang Bo, “CamShift tracker based on image moments,” Journal of Beijing University of Technology, v 38, n 1, p 105-109, 2012.
[5]. W. Xiangyu, and L. Xiujuan, "The study of moving target tracking based on Kalman-CAMShift in the video," 2nd International Conference on Information Science and Engineering (ICISE), pp.1-4, 2010.
[6]. J. Yin, Y. Han, J. Li, and A. Cao, "Research on Real-Time Object Tracking by Improved CAMShift," International Symposium on Computer Network and Multimedia Technology, pp.1-4, 2009.
[7]. G. J. Allen, Y. D. Richard Xu and S. Jin Jesse, “Object Tracking Using CAMShift Algorithm and Multiple Quantized Feature Spaces”, Inc. Australian Computer Society, vol.36, 2004.
[8]. Li Chao, Liu Tiegen, Liu Hongli, et al., “Face tracking based on Haar detection and improved Camshift algorithm,” Journal of Optoelectronics Laser, v 22, n 12, p 1852-1856, 2011.
[9]. Comaniciu Dorin, Ramesh Visvanathan, and Meer Peter, “Kernel-based object tracking,” IEEE Trans. on Pattern Analysis and Machine Intelligence, v25, n5, pp.564-577, 2003.
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Published
2019-09-08
How to Cite
Babu, M. S. I. (2019). Moving Object Tracking Based on Camshift Algorithm. European Journal of Applied Sciences, 7(4), 01–05. https://doi.org/10.14738/aivp.74.6702
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