• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 图形与图像 • 上一篇    下一篇

改进STC和SURF特征联合优化的目标跟踪算法

黄云明1,张晶1,2,喻小惠1,陶涛3,龚力波4   

  1. (1.昆明理工大学信息工程与自动化学院,云南 昆明 650500;2.云南枭润科技服务有限公司,云南 昆明 650500;
    3.云南省信息技术发展中心,云南 昆明 650228;4.云南省农村科技服务中心,云南 昆明 650021)
     
  • 收稿日期:2018-12-05 修回日期:2019-02-27 出版日期:2019-10-25 发布日期:2019-10-25
  • 基金资助:

    国家自然科学基金(61562051);云南省技术创新人才项目(2019HB113)

A target tracking algorithm based on joint
optimization of improved STC and SURF features

HUANG Yun-ming1,ZHANG Jing1,2,YU Xiao-hui1,TAO Tao3,GONG Li-bo4   

  1. (1.Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500;
    2.Yunnan Xiaorun Technology Service Co.Ltd.,Kunming 650500;
    3.Yunnan Information Technology Development Center,Kunming 650228;
    4.Yunnan Rural Science and Technology Service Center,Kunming 650021,China)
  • Received:2018-12-05 Revised:2019-02-27 Online:2019-10-25 Published:2019-10-25

摘要:

针对传统时空上下文目标跟踪(STC)算法中目标窗口不能适应目标尺度变化,导致对目标针对性不强等问题,提出改进STC和SURF特征联合优化的目标跟踪算法(STC-SURF)。首先利用加速稳健(SURF)特征算法对相邻的2帧图像提取特征点并进行匹配,再通过随机抽样一致(RANSAC)算法消除误匹配,
提高匹配精度。进而根据2帧图像中匹配特征点的变化对目标窗口进行调整。最终对STC算法中模型的更新方式进行优化以提高跟踪结果的准确性。实验结果表明,STC-SURF算法能够适应目标尺度变化,并且其目标跟踪成功率优于TLD算法和传统STC算法的。
 
 

关键词: 自适应, 尺度变化, 目标跟踪, SURF特征, 时空上下文

Abstract:

Aiming at the problem that the target window cannot adapt to target scale change in the traditional spatio-temporal context tracking (STC) algorithm, which leads to inaccurate targeting, we propose a target tracking algorithm based on joint optimization of improved STC and SURF features (STC-SURF). Firstly, the feature points of two adjacent frames are extracted and matched by the speeded up robust feature (SURF) algorithm, and the random sample consensus (RANSAC) matching algorithm is used to eliminate the mismatch and increase the matching precision. Furthermore, the target window is adjusted according to the change of the matching feature points in the two frames of the image, and then outputted. Finally, the update method of the model of the STC algorithm is optimized to increase the accuracy of the tracking result. Experimental results show that the STC-SURF algorithm can adapt to the target scale change, and the target tracking success rate is better than the target-learning detection (TLD) algorithm and the traditional STC algorithm.
 

Key words: adaptive, scale change, target tracking, SURF feature, spatio-temporal context