Moving Object Tracking Based on Camshift Algorithm
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.
. 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.
. 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.
. 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.
. 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.
. 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.
. 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.
. 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.
. 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.
Copyright (c) 2019 Advances in Image and Video Processing
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors wishing to include figures, tables, or text passages that have already been published elsewhere are required to obtain permission from the copyright owner(s) for both the print and online format and to include evidence that such permission has been granted when submitting their papers. Any material received without such evidence will be assumed to originate from the authors.
All authors of manuscripts accepted for publication in the journal Transactions on Networks and Communications are required to license the Scholar Publishing to publish the manuscript. Each author should sign one of the following forms, as appropriate:
License to publish; to be used by most authors. This grants the publisher a license of copyright. Download forms (MS Word formats) - (doc)
Publication agreement — Crown copyright; to be used by authors who are public servants in a Commonwealth country, such as Canada, U.K., Australia. Download forms (Adobe or MS Word formats) - (doc)
License to publish — U.S. official; to be used by authors who are officials of the U.S. government. Download forms (Adobe or MS Word formats) – (doc)
The preferred method to submit a completed, signed copyright form is to upload it within the task assigned to you in the Manuscript submission system, after the submission of your manuscript. Alternatively, you can submit it by email firstname.lastname@example.org