计算机应用 ›› 2013, Vol. 33 ›› Issue (02): 499-502.DOI: 10.3724/SP.J.1087.2013.00499

• 多媒体处理技术 • 上一篇    下一篇

基于快速鲁棒特征的CamShift跟踪算法

王晋疆1,2,刘阳1,2,吴明云2   

  1. 1. 光电信息技术教育部重点实验室(天津大学),天津 300072
    2. 天津大学 精密仪器与光电子工程学院, 天津 300072
  • 收稿日期:2012-08-08 修回日期:2012-09-06 出版日期:2013-02-01 发布日期:2013-02-25
  • 通讯作者: 刘阳
  • 作者简介:王晋疆(1974-),男,河南林州人,副教授,博士,主要研究方向:光电信息处理、图像处理;
    刘阳(1989-),男,江西丰城人,硕士研究生,主要研究方向:视频图像处理、目标跟踪;
    吴明云(1988-),男,安徽马鞍山人,硕士研究生,主要研究方向:图像处理。

CamShift tracking algorithm based on speed-up robust features

WANG Jinjiang1,2,LIU Yang1,2,WU Mingyun1   

  1. 1. College of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China
    2. Key Laboratory of Opto-electronics Information Technology, Ministry of Education (Tianjin University), Tianjin 300072, China
  • Received:2012-08-08 Revised:2012-09-06 Online:2013-02-01 Published:2013-02-25
  • Contact: LIU Yang

摘要: 为了解决CamShift算法由于对颜色敏感导致的跟踪效果变差或失效的问题,提出一种基于局部特征匹配的CamShift跟踪算法。采用快速鲁棒特征(SURF)方法在多通道图像的目标区域和搜索区域提取包含图像信息的局部特征点,并利用近似最近邻搜索对特征点进行匹配;使用提纯后的匹配结果得到特征点的位置、尺度及方向信息,对CamShift方法进行约束和更新,以提高跟踪精度和稳定性。实验结果表明,与经典CamShift算法和同类的改进算法相比,该算法能够较好地实现对复杂背景下旋转和放缩运动目标的实时跟踪。

关键词: 目标跟踪, 快速鲁棒特征, 特征匹配, 均值漂移, 尺度不变特征变换

Abstract: In order to deal with the poor or invalid tracking performance caused by the color sensitivity of Continuously adaptive Mean Shift (CamShift) algorithm, a new CamShift tracking algorithm based on local feature matching was proposed. The new algorithm used the method of Speeded-Up Robust Feature (SURF) to extract the local feature points containing the image information from the target and searched areas of multi-channel images, and then matched the feature points by the method of approximate nearest neighbor searching. The location, scale and orientation information of the feature points were obtained utilizing the purified matching results, therefore the CamShift method was constrained and updated to improve the accuracy and stability of tracking. The experimental results show that the new algorithm can outperform the classic CamShift algorithm and the similar improved algorithms for rotating and zooming objects against complex backgrounds in real-time tracking.

Key words: object tracking, Speeded Up Robust Feature (SURF), feature matching, mean shift, Scale Invariant Feature Transform (SIFT

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