尺度感知的分块协同式相关滤波跟踪算法

基于分块的相关滤波跟踪算法在处理目标尺度变化和遮挡问题时,对局部子块跟踪状态的评估及局部子块与尺度变化的关系刻画不够准确.针对此问题,文中提出尺度感知的分块协同式相关滤波跟踪算法.首先提出结合时序平滑约束的局部子块遮挡判别方法,改进现有算法的评分策略.设计子块协同运动策略,使被遮挡或形变的子块跟随未被遮挡的子块趋向正确的位置.同时发现跟踪过程中子块聚散变化的分布位置与目标尺度之间的比例关系,实现对目标尺度变化的感知和大小估计.实验表明,文中算法性能较优.

Scale-Aware Partition-Based Cooperative Correlation Filter Tracking Algorithm

When partition-based correlation filtering tracking algorithm is applied to deal with scale changes and target occlusion problems, the estimation of local sub-block state tracking and the relationship between local sub-block and scale change are inaccurate.To address this issue, a scale-aware partition-based cooperative correlation filter tracking algorithm is proposed. A method of local sub-block occlusion discrimination based on time-sequence smooth constraint is adopted, and the scoring strategy of the existing algorithm is improved. A cooperative motion strategy for sub-blocks is then designed to make the occluded or the deformed sub-blocks follow the normal ones to their due positions. And the ratio between target scale and distributed location of sub-blocks aggregation and dispersion is discovered to perceive target scale changes and estimate sizes. Experiments indicate that the proposed algorithm achieves better performance.