关键词: 目标跟踪, 特征融合, 自适应加权, 置信度, 相关滤波

Abstract: Nowadays, the machine vision is widely used, and there will be various challenges in the process of video target tracking. In order to solve the problem of weak robustness, model and scale update mechanism, a correlation filter target tracking algorithm that combines the adaptive weighted feature fusion method and the confidence degree model updating mechanism is proposed. The algorithm uses complementary gradient and color features for feature fusion. By calculating the filtering response of each feature, it determines their weight in the fusion feature in next frame, so as to highlight the dominant feature and make the target more distinguishable from the background. At the same time, the confidence level is introduced. The peak side lobe ratio is used as the evaluation criterion to prevent model updating from causing similar interference and occlusion, which can improve accuracy. Finally, a new scale update method is proposed, which simplifies the redundant code to reduce the time cost as well as make the tracking more precise. Experimental results show that the proposed algorithm is better in accuracy and success rate than several existing correlation filtering algorithms, and it is more robust to deal with similar target interference and occlusion. This paper improves the correlation filtering algorithm, the feature fusion and updating mechanism is added, so that the algorithm improves the tracking effect and has certain application value, it has certain application value.

Key words: object tracking, feature fusion, adaptive weighting, confidence level, correlation filter