Crowd Abnormal Event Detection Based on Crowd Density

Abstract

Abstract This paper proposes a crowd abnormal event detection algorithm based on the change of crowd density. This method does not use the optical flow of the traditional model, thus the algorithm is fast. The algorithm first estimates the crowd density in the scene, then uses the change of crowd density as the feature representation of the crowd and constructs 3D feature blocks by adding time axis attributes. Finally, a single classifier is used to classify the 3D feature blocks for detecting crowd abnormal events in videos. Because the algorithm uses the change of crowd density in the scene as the feature representation of the crowd, the algorithm will not be disturbed by the motion of vehicles and other objects in the scene. The experimental results show that the proposed algorithm runs faster and has higher accuracy than the optical flow algorithm.