Collective Density Clustering for Coherent Motion Detection

Yunpeng Wu, Yangdong Ye, Chenyang Zhao, Zenglin Shi

Abstract: Coherent motion detection remains a challenging problem due to the inherent complexity and vast diversity found in crowded scenes. Inspired by divide-and-conquer strategy, we desire to detect coherent motion from both local and global level. In this study, a novel collective density clustering (CDC) method is proposed to detect local and global coherent motion. We creatively define a collective density to discover underlying ordered density estimation, and subsequently a novel collective clustering algorithm is introduced, which is able to identify collective subgroups rapidly. Considering the complex interaction among subgroups, we present a hierarchical Union-Find-based collective merging algorithm to recognize coherent motion by merging collective subgroups. Our method is very efficient and effective. Experimental results on several challenging video datasets demonstrate that the proposed CDC achieves better results than state-of-the-art works, and multiple times or even tens of times faster. The proposed framework shows potential to be further applied to other problems (e.g., affine motion segmentation), related to local and global clustering.