Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (11): 71-79.DOI: 10.3778/j.issn.1002-8331.2202-0099

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Correlation Filter for Object Tracking Method Based on Spare Representation

SHE Xiangyang, LUO Jiaqi, REN Haiqing, CAI Yuanqiang   

  1. 1.College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China
    2.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
    3.School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Online:2023-06-01 Published:2023-06-01



  1. 1.西安科技大学 计算机科学与技术学院,西安 710054
    2.中国科学院大学 计算机科学与技术学院,北京 100049
    3.北京邮电大学 计算机学院,北京 100876

Abstract: Aiming at the problem that the object tracking methods based on correlation filter is easily affected by the distractive features in complex scenes such as object deformation and background interference, which leads to the tracking failure, a correlation filter for object tracking method based on sparse representation is proposed. The method combines correlation filter with sparse representation by using L1 norm to sparse constrain the correlation filter in the objective function, so that the trained correlation filter only contains the key features of the object. At the same time, different penalty parameters are assigned to the correlation filter coefficients according to spatial position of the correlation filter coefficients, and the alternating direction method of multipliers(ADMM) is used to solve the correlation filter. The experimental results show that:the method has the best precision and success rate in comparison with five object tracking methods based on correlation filter on three commonly used datasets. At the same time, the method has good robustness to the distractive features in complex scenes, and can meet the real-time requirements.

Key words: object tracking, correlation filter, sparse representation, alternating direction method of multipliers

摘要: 针对相关滤波跟踪算法在目标形变、背景干扰等复杂场景下,易受干扰特征影响导致跟踪失败的问题,提出了基于稀疏表示的相关滤波目标跟踪算法。该算法将稀疏表示与相关滤波相结合,在目标函数中引入L1范数惩罚项,使训练出的相关滤波器只含有目标的关键特征,同时根据相关滤波系数的空间位置为其分配不同的惩罚参数,并采用交替方向乘子法(alternating direction method of multipliers,ADMM)求解相关滤波器。实验结果表明:该算法在三个常用数据集上,与五种相关滤波跟踪算法相比,具有最高的精确度和成功率,且对复杂场景中的干扰特征具有良好的鲁棒性,同时能够满足目标跟踪实时性的要求。

关键词: 目标跟踪, 相关滤波, 稀疏表示, 交替方向乘子法