Group detection assisted by density map
Yuan Xiao, Yihua Tan · 2020
Group detection is crucial component in intelligent video surveillance, which can capture crowd motion and directly apply to emergency security in complex scenes, thus it has attracted plenty of attention in the related fields. However, the existing works cannot fully utilize the deep and precise features of the crowd. Recently, with the rapid development of deep learning and the promotion of challenging datasets, crowd density estimation has achieved the desired accuracy in single image. Since density maps can provide a high-level semantic information for the crowd, in this paper, a density map assisted scene analysis method is proposed to detect the groups in crowd scenes. The main contributions in this study are threefold: (1) Using density map-based super-pixel segmentation method to obtain the multiple image patches, which are taken as the next research objects; (2) A group detection method based on multi-view clustering is proposed. The density maps are used to construct similar graphs from the aspects of interaction, spatial distribution, motion distribution and motion pattern. (3) A post-processing strategy is designed to combine the groups with higher relevance to determine the final group. The experimental results show that the method can accurately detect the groups in image sequence. Furthermore, compared with the existing methods, the proposed method achieves better performance on the CUHK Crowd Dataset.