关键词: 目标跟踪, 相关滤波器, 边界效应, 深度特征, 交替方向乘子, 样本相似性

Abstract: This paper is aiming at the bottleneck problem of discriminative target tracking using correlation filter. On the period of learning and updating on correlation filter, error is likely to be induced into filter, and fatal cumulation will finally cause tracker inefficiency. It is attributed to the boundary effect caused by the rapid movement of the target. Based on acquisition of depth learning features and sample similarity measure, an improved correlation filter target tracking algorithm with alternating direction method of multipliers is proposed in this paper. DCNN depth features are selected to effectively represent the initial state of the object to be tracked. The algorithm uses sample similarity matching with semi supervised learning in online classification process. The above methods assist in solving the self-learning problem of the correlation filter in the learning process. The proposed object tracking algorithm is especially suited to machine learning process where samples are continually acquired and memory storage is limited. On the complicated scenes such as object speed drastically changing and object partially blocked, etc., success tracking rate is updated by importing the proposed object tracking algorithm. When target vehicle is partially blocked, the experiments on standard testing videos involving VOT2016 demonstrate that success tracking rate of the proposed DA-CFT tracking algorithm is raised to 85.6%~91.0%, compared with 60.4%~73.4% of CN algorithm, 67.2%~82.9% of SAMF algorithm and 80.9%~88.1% of STC algorithm.

Key words: object tracking, correlation filter, boundary effect, depth feature, alternating direction multiplier, sample similarity