English

应用科学学报 ›› 2013, Vol. 31 ›› Issue (1): 104-110.doi: 10.3969/j.issn.0255-8297.2013.01.017

• 信号与信息处理 • 上一篇    

多模块稀疏投影在密集场景目标跟踪中的应用

邵洁1， 董楠2   

  1. 1. 上海电力学院电子与信息工程学院，上海200090
    2. 中国科学院上海高等研究院，上海201203
  • 收稿日期:2011-08-01 修回日期:2012-01-25 出版日期:2013-01-31 发布日期:2012-02-25
  • 作者简介:邵洁，博士，讲师，研究方向：图像处理与计算机视觉，E-mail: shaoj.shiep@gmail.com
  • 基金资助:

    国家自然科学基金(No.61105016)资助

Visual Tracking in Crowded Scenes with Multi-part Sparse Representation

SHAO Jie 1, DONG Nan2   

  1. 1. School of Electronic and Information Engineering, Shanghai University of Electric Power,
    Shanghai 200090, China
    2. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201203, China
  • Received:2011-08-01 Revised:2012-01-25 Online:2013-01-31 Published:2012-02-25

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摘要： 提出了一种包含随机运动的复杂密集场景下的目标跟踪方法. 在跟踪算法中，将稀疏模型与多模块彩色特征相结合，并通过将其转化为l1正则化最小二乘问题实现对特征的稀疏投影. 跟踪过程中利用粒子滤波得到预测跟踪点，并将对应于最小投影差的预测点模块作为最优跟踪. 为适应特征变化，在跟踪完成后根据新的跟踪结果自动更新目标模板. 大量包含遮挡和光照变化的不同类型密集场景测试验证了该方法的有效性，与其他算法的比较说明了其优良性能.

关键词: 视频跟踪, 稀疏表示, 密集场景, 多模块彩色特征, 粒子滤波

Abstract:  This paper presents a target tracking framework applicable to complex crowded scenes with random movements. A robust tracking algorithm using a local sparse appearance model associated with a multi-part color representation is proposed. Sparsity is achieved by solving an l1 regularized least squares problem.Candidates with the smallest projection error is taken as the tracking result. All candidates are drawn based on a density distribution in a Bayesian state inference framework. The target templates are dynamically updated to adapt appearance variation at the end of a tracking iteration. We test the approach on numerous videos including different type of very crowded scenes with serious occlusion and illumination variation. The proposed approach demonstrates excellent performance in comparison with previous methods.

Key words: visual tracking, sparse representation, crowded scenes, multi-part color feature, particle filter

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