关键词: 图像处理, 目标跟踪, 相关滤波, 异常适应性, 通道适应性

Abstract: The spatial regularization tracking algorithm ignores channel and anomalies adaptation, which can easily lead to tracking failure in complex tracking scenarios such as illumination changes, occlusions, and motion blur. To address these problems, this paper proposes a tracking algorithm with adaptive channel and anomaly adaptation. Firstly, the appearance model of the target is built by the gradient and color features. Secondly, a channel weighting strategy is proposed. An adaptive channel regularizer is constructed to optimize the channel weights simultaneously in the training phase to reduce the impact of redundant information in multi-channel features and channel reliability changes on the tracking performance. Then, the adaptive anomaly regularizer is constructed to constrain the response map's abnormal changes and improve the tracker??s robustness when the region changes rapidly. Finally, the filter is correlated with the current sample to obtain the target scale and position in the detection stage. The peak-versus-noise smoothness index of the response map is calculated to judge the occlusion and exclude low-quality samples to enhance the abnormal adaptation when occlusion occurs. Comparative experiments with several mainstream methods are performed on the OTB50, OTB100 and TC-128 benchmarks. Experimental results show that the proposed algorithm has better robustness in complex scenarios such as illumination variation, occlusion, motion blur, etc. The tracking success rate is higher than similar algorithms, and the accuracy is higher.

Key words: image processing, object tracking, correlation filtering, anomaly adaptation, channel adaptation