Real-time crowd detection based on gradient magnitude entropy model
Huiyuan Fu, Huadóng Ma · 2014
Reliable and real-time crowd detection is one of the most important tasks in intelligent video surveillance system. Previous works focus on counting the number of pedestrians in the crowd directly or use holistic features of crowd scenes for crowd detection. However, the former methods will be invalid in complex crowded scenes, and the latter methods will be confused for feature selection. In this paper, we propose a simple but effective model - Gradient Magnitude Entropy (GME) model for crowd detection. Our model is based on a key observation - the value of GME in a region which will increase as the number of pedestrians grows. Thus, we can estimate the degree of crowd when the value of GME is larger than some threshold, without counting the number of pedestrians. Extensive experiments show that our GME model outperforms state-of-the-art techniques on several challenging datasets. Furthermore, our method can process in real time for practical surveillance applications.