CSA  >> Vol. 7 No. 9 (September 2017)

    基于Mean Shift的粒子滤波视频目标跟踪算法
    Research on Video Target Tracking Based on Particle Filter and Mean-Shift

  • 全文下载: PDF(3060KB) HTML   XML   PP.834-849   DOI: 10.12677/CSA.2017.79096  
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王思雅:四川大学计算机学院,四川 成都 ;
冯子亮:视觉合成图形图像技术国家重点学科实验室,四川 成都

粒子滤波目标跟踪均值漂移Particle Filter Target Tracking Mean Shift


目标跟踪一直是计算机视觉领域的重要研究方向,由于拍摄环境的多变性、目标运动状态的不固定性、目标间的相互遮挡以及相似物的干扰等因素使视频目标跟踪变得更加复杂。本文以粒子滤波算法为框架研究目标跟踪算法,重点对目标特征提取、模型间相似度的度量方法、控制跟踪过程中粒子数量进行研究,以提高算法的鲁棒性、准确性和实时性。提出了一种基于Mean Shift的粒子滤波视频目标跟踪算法。该算法为了减少光照的干扰,采用HSV颜色空间的核函数加权颜色直方图来描述颜色特征,在粒子初始化和重采样后采用一次Mean Shift算法的迭代过程将一些作用弱的粒子不参与计算,以减少计算量。完成一次目标估计后更新目标模型,以适应目标在运动过程的变化。对比实验结果表明,本算法具有较好的实时性。

Target tracking has been already a research direction in the field of pattern recognition and computer vision. Especially, video target tracking has become a focus of study. Target tracking plays an important role in the intelligent monitoring, traffic monitoring, traffic statistics, and so on, due to the variability of shooting environment, the fluid of target motion state, the shading of targets and the interference of likeness, etc. These factors make video target tracking more complicated. Video target tracking can be defined as select single or multiple target from the video sequences filming with cameras, and can give target location accurately and timely, and then get target motion trajectory and its motor habit. Mainly in the framework of particle filter algorithm, this paper analyzes the factors which affect the effect of particle filter tracking, and focuses on the study of the target feature extraction, the similarity measurement method between models, controlling the number of particles, in order to improve the robustness, accuracy and instantaneity of the algorithm. Particle filter video target tracking algorithm based on Mean Shift is proposed. The algorithm uses an iterative process of Mean Shift algorithm after the particle initialization and resampling to reduce the amount of calculation. The algorithm updates the target model after estimating location of the target, in order to adapt to changes of the target. The experimental results show that the effect of the particle filter tracking based on Mean Shift is better, and it needs shorter time.

王思雅, 冯子亮. 基于Mean Shift的粒子滤波视频目标跟踪算法[J]. 计算机科学与应用, 2017, 7(9): 834-849. https://doi.org/10.12677/CSA.2017.79096


[1] Alam, I. (2015) Object Tracking in Video Sequences Using Information Fusion Principles. IEEE Transactions on 5th Computer Science and Electronic Engineering Conference, Colchester, UK, 17-18 September 2013, 146-151.
[2] 胡世强, 敬忠良. 粒子滤波算法综述[J]. 控制与决策, 2005, 20(4): 361-365.
[3] Tang, D. and Zhang, Y.J. (2011) Combining Mean-Shift and Particle Filter for Object Tracking. International Conference on Image and Graphics, Hefei, 12-15 August 2011, 771-776.
[4] 张颖颖, 王红娟, 黄义定. 基于Mean Shift和粒子滤波的行人目标跟踪方法[J]. 计算机与现代化, 2012(3): 40-43.
[5] 马丽, 常发亮, 乔谊正. 基于均值漂移算法和粒子滤波算法的目标跟踪[J]. 模式识别与人工智能, 2006, 19(6): 787-793.
[6] 顾幸芳, 茅耀斌, 李秋洁. 基于Mean Shift的视觉目标跟踪算法综述[J]. 计算机科学, 2012, 39(12): 16-22.
[7] Comaniciu, D. and Meer, P. (2002) Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619.
[8] Comaniciu, D., Ramesh, V. and Meer, P. (2000) Real-Time Tracking of Non-Rigid Objects Using Mean Shift. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, SC, 15 June 2000.
[9] Maggio, E. and Cavallaro, A. (2005) Multi-Part Target Representation for Color Tracking. Proceeding of IEEE International Conference on Image Proceeding, Genova, 14 September 2005.
[10] Zhang, X. and Ming, Y. (2010) An Improved Mean Shift Tracking Algorithm Based on Color and Texture Feature. Proceeding of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 11-14 July 2010, 38-43.
[11] Du, K., Ju, Y. and Jin, Y. (2012) Mean Shift Tracking Algorithm with Adaptive Block Color Histogram. IEEE Transactions on Special Fund for Basic Scientific Research in the Central University, Yichang, 21-23 April 2012, 2692-2695.
[12] He, J.W. and Yang, Y.Y. (2014) Multi-Iterative Tracking Method Using Mean Shift Based on Kalman Filter. IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Guilin, 5-8 August 2014, 22-27.