Coherent Motion Detection with Collective Density Clustering

Detecting coherent motion is significant for analysing the crowd motion in video applications. In this study, we propose the Collective Density Clustering(CDC) approach to recognize both local and global coherent motion having arbitrary shapes and varying densities. Firstly, the collective density is defined to reveal the underlying patterns with varying levels of density. Based on collective density, the collective clustering algorithm is further presented to recognize the local consistency, where density-based clustering is more adaptive to recognize clusters with arbitrary shapes. This algorithm has salient properties including single step of clustering process, automatical decision of clustering number and accurate identification of outliers. Finally, the collective merging algorithm is introduced to fully characterize the global consistency. Experiments on diverse crowd scenes, including pedestrians, traffic and bacterial colony, demonstrate the effectiveness for coherent motion detection. The comparisons show that our approach outperforms state-of-the-art coherent detection techniques.