Tracking People in Dense Crowds Using Supervoxels

Abstract

The demand for people tracking in dense crowds is increasing, but it is a challenging problem in the computer vision field. "Crowd tracking" is extremely difficult because of hard occlusions, various motions and posture changes. In particular, we need to handle occlusions for more robust tracking. This paper discusses robust crowd tracking based on a combination of supervoxels and optical flow tracking. The SLIC based supervoxel algorithm adaptively estimates the boundary between a person and a background. Therefore, the combination of supervoxels and optical flow tracking becomes a highly reliable approach for crowd tracking. In tracking experiments, high performance is achieved for the UCF crowd dataset.