Measuring orderliness based on social force model in collective motions
Collective motions, one of the coordinated behaviors in crowd system, widely exist in nature. Orderliness characterizes how well an individual will move smoothly and consistently with his neighbors in collective motions. It is still an open problem in computer vision. In this paper, we propose an orderliness descriptor based on correlation of interactive social force between individuals. In order to include the force correlation between two individuals in a distance, we propose a Social Force Correlation Propagation algorithm to calculate orderliness of every individual effectively and efficiently. We validate the effectiveness of the proposed orderliness descriptor on synthetic simulation. Experimental results on challenging videos of real scene crowds demonstrate that orderliness descriptor can perceive motion with low smoothness and locate disorder.