Perceiving the crowd behavior is very important for social cloud robots, who serve as guiders at transportation junctions. In this paper, we propose a real-time algorithm based on background modeling to detect collective motions in complex scenes. The proposed algorithm not only avoids unstable foreground extraction, but also has low computational complexity. To detect the abnormal crowd escape, we refer to the definition of the moving energy of patches and use the energy histogram of the patches to effectively and accurately represent the crowd distribution information in the crowd scenes. We have applied the proposed algorithm to the real surveillance videos which contain the aggregation and dispersion events. The experimental results show the significant outperformance of the proposed algorithm in comparison to the-state-of-the-art approach.
['Dekun Hu', 'Binghao Meng', 'Shengyi Fan', 'Hong Cheng', 'Lu Yang', 'Yanli Ji']
Real-Time Understanding of Abnormal Crowd Behavior on Social Robots