Joint crowd detection and semantic scene modeling using a Gestalt laws-based similarity
Weiqi Zhao, Zhang Zhang, Kaiqi Huang · 2016
This paper presents a novel approach to detecting crowd groups and learning semantic regions with a Gestalt laws-based similarity. Different from the existing approaches based on optical flows or complete trajectories, our model adopts tracklets as the original input, because they carry more detailed information. Though those tracklets do not appear in the same duration, they are more robust to noise in crowd scene. According to the Gestalt laws of grouping, we propose three priors to define a unified similarity measure to calculate the affinities of pairs of original tracklets and pairs of representative tracklets in crowd groups. Therefore, the short-term crowd groups and the long-term semantic paths in crowded scene can be detected by a bottom-up hierarchical clustering algorithm simultaneously. Extensive experiments on hundreds of video clips demonstrate that our approach is effective and reliable for crowd detection and semantic scene understanding.