基于子空间和直方图的多记忆自适应相关滤波目标跟踪算法

为了增强相关滤波算法(CF)在目标遮挡或背景干扰情况下跟踪的鲁棒性,提出基于子空间和直方图的多记忆自适应相关滤波目标跟踪算法.首先,针对CF使用的模板单一无法应对不同时期相邻帧目标表现的差异,提出利用随机更新策略学习多个目标模板,应对不同时期的目标变化.然后,针对不同的更新模板得到多个候选目标,利用子空间学习上一帧的表示系数,综合判断候选目标的准确性.同时,因为CF与子空间表示均利用模板判断跟踪结果,对背景杂乱等情况判断容易造成偏差,所以引入颜色直方图,利用统计特征作为独立的判断依据,增强算法对候选目标判断结果的准确性.在标准视频集上的实验表明,文中算法具备一定的抗遮挡及抗背景干扰能力.

Object Tracking with Multiple Memory Learning and Adaptive Correlation Filter Based on Subspace and Histogram

To enhance the tracking robustness of the correlation filter(CF) in occlusion and background clutter, an algorithm for object tracking with multiple memory learning and adaptive correlation filter based on subspace and histogram is proposed. CF cannot cope with target appearance difference of adjacent frames in the different periods using a single template. A strategy of random updates is proposed to learn multiple target templates and adapt to the variation of target. Several candidate targets are obtained by random updates and the representation coefficients of the previous frame is learned by subspace structure to synthetically judge the accuracy of current candidates. Since CF and subspace representations are sensitive to background clutter, the color histogram is introduced to achieve complementary appearance representation. The statistical histogram is used as the independent judgment basis to improve the accuracy of the algorithm in judging the candidate target. The experimental results on video sequences demonstrate that the proposed algorithm has the ability of anti-occlusion and anti-background interference.