自适应特征选择的相关滤波跟踪算法

Correlation Filter Tracking Algorithm for Adaptive Feature Selection

针对相关滤波方法对快速运动与快速变形的目标跟踪稳定性较差的问题,提出一种自适应特征选择的相关滤波跟踪算法。利用位置滤波器和颜色概率模型提取候选区域中的基础特征,对基础特征以不同的权重分配方式进行融合,得到多个融合特征。对融合特征进行可信度判定,选择可信度较高的融合特征作为当前帧的跟踪特征,估计出目标的候选位置。若最高可信度低于可信度阈值,启动检测器重新检测目标位置,否则候选位置即为目标最终位置。与此同时,对目标模型进行更新,确保模型对目标描述的准确性。在标准数据集OTB50和OTB100上进行大量实验,测试结果表明,所提出的跟踪方法在运动模糊、光照变化、快速运动等条件下具有较高的跟踪准确率和较好的稳健性。

The conventional correlation filtering methods are known to demonstrate poor tracking stability for fast moving and fast deforming targets. Therefore, this paper proposes a correlation filter tracking algorithm for adaptive feature selection. First, the basic features are extracted in the candidate regions using a position filter and a color probability model and fused in different weight combinations to obtain multiple fusion features. Then, the credibility of the fusion features is determined and the features with relatively high credibility are selected as the tracking features of the current frame to estimate the candidate position of the target. Finally, if the maximum credibility is less than the credibility threshold, the detector is activated to redetect the target position;otherwise, the candidate position is just the final position. Meanwhile, the target model is updated to ensure the accuracy of target description. The experimental results on the standard OTB50 and OTB100 datasets show that the proposed tracking method has relatively high tracking accuracy and good robustness under the conditions of motion blurring, illumination variation, and fast motion.