Hybrid Histogram of Oriented Optical Flow for Abnormal Behavior Detection in Crowd Scenes

Abnormal behavior detection in crowd scenes has received considerable attention in the field of public safety. Traditional motion models do not account for the continuity of motion characteristics between frames. In this paper, we present a new feature descriptor, called the hybrid optical flow histogram. By importing the concept of acceleration, our method can indicate the change of speed in different directions of a movement. Therefore, our descriptor contains more information on the movement. We also introduce a spatial and temporal region saliency determination method to extract the effective motion area only for samples, which could effectively reduce the computational costs, and we apply a sparse representation to detect abnormal behaviors via sparse reconstruction costs. Sparse representation has a high rate of recognition performance and stability. Experiments involving the UMN datasets and the videos taken by us show that our method can effectively identify various types of anomalies and that the recognition results are better than existing algorithms.