基于点轨迹的核相关滤波器跟踪算法

Kernelized correlation tracking based on point trajectories

视频跟踪是计算机视觉领域的重要研究方向之一。然而,目标跟踪过程中的遮挡问题会导致视频中目标信息的缺失,因此许多算法无法稳定地跟踪目标。针对遮挡问题,提出一种基于点轨迹的核相关滤波器跟踪算法。通过对目标及其周围物体长期运动轨迹的估计,使用谱聚类对点轨迹进行标记,以区分目标区域与背景区域,从而判断目标是否发生遮挡或漂移,决定是否启用重检测机制对目标进行检测。实验证明,该方法对遮挡和漂移问题具有较强的顽健性。

Visual tracking is one of the most important directions in computer vision. However, many state-of-the-art algorithms cannot track the interested object reliably due to occlusion during tracking process, which leads to deficiency of object information. In order to solve occlusion problem, a kernelized correlation tracking method based on point trajectories was proposed. Through analyzing long-term motion cues of the local information, point trajectories were labeled by spectral clustering. These labeled points were used to differentiate the foreground and background objects and thus detect whether the target was occluded or drifts. If drifting and occlusion occur, re-detection was used to detect the re-entering of the target. Experimental results show that the proposed algorithm can handle occlusion and drifting problems effectively.