关键词: 鉴别回归模型, 多线索特征, 方向梯度直方图, 颜色名, 相关滤波跟踪算法, 自学习特征

Abstract: The Correlation Filter（CF） tracking algorithms have achieved outstanding performance by using efficient discriminative regression model and multi-cue features, such as Histograms of Oriented Gradients（HOG） and Color Names（CN）. However, the performance still suffers from insufficient discriminative information during appearance variations. To mitigate this problem, a Self-Learning based Discriminative Correlation Filter tracking algorithm（SLDCF） is proposed. The self-learning feature is obtained by exploring the collaborative representations between successive frames. It extracts the information from target variation and alleviates the impact from background. The experimental results on the standard video benchmarking dataset demonstrate the effectiveness and robustness of the proposed algorithm and its superior performance in comparison with other traditional correlation filter tracking algorithms.

Key words: discriminative regression model, multi-cue features, histograms of oriented gradients, color names, correlation filter tracking algorithms, self-learning features