Density-Based Manifold Collective Clustering for Coherent Motion Detection

Detecting coherent motion remains a challenging problem with important applications for the video surveillance and understanding of crowds. In this study, we propose the Density-based Manifold Collective Clustering approach to recognize both local and global coherent motion having arbitrary shapes and varying densities. Firstly, a new manifold distance metric is developed to reveal the underlying patterns with topological manifold structure. Based on the novel definition of collective density, the Density-based collective clustering algorithm is further presented to recognize the local consistency, where its strategy is more adaptive to recognize clusters with arbitrary shapes. Finally, considering the complex interaction among subgroups, a hierarchical collectiveness merging algorithm is introduced to fully characterize the global consistency. Experiments on several challenging video datasets demonstrate the effectiveness of our approach for coherent motion detection, and the comparisons show its superior performance against state-of-the-art competitors.