Real-time frequency-based detection of a panic behavior in human crowds

Abstract

The real-time detection of a panic behavior in a human crowd is of a high interest as it helps alleviating crowd disasters and ensures that timely appropriate action will be taken. However, the fast analysis of video sequences to detect abnormal behaviours is one of the most challenging tasks for computer vision experts. While many research works propose off-line solutions, few studies investigate the real-time analysis of crowded scenes. This may be due to the fact that detecting a panic behaviour is closely related to the analysis of the crowd dynamics, which commonly necessitates heavy computations. In order to alleviate this problem, we propose a real-time panic detection technique that analyzes the crowd movements based on a simple and efficient solution. The key idea of the proposed approach consists of analyzing the interactions between moving edges along the video in the frequency domain. Our contribution is threefold. First, moving edges are considered for analysis along the video. Second, when a panic situation occurs within a human crowd, it leads to interactions between people that are different from those that occur during a normal situation. Therefore, to reveal such a behavior, a new frequency based-feature is proposed. To select the most appropriate frequency domain, the fast fourier transform, the discrete cosine transform and the discrete wavelet transform are investigated. Third, two different formulations of the problem of detecting a panic are explored. The experimental evaluation of the proposed technique shows its outperforming compared to the state-of-the-art approaches in terms of detection rates and execution time.