Gaussian-Poisson mixture model for anomaly detection of crowd behaviour

This paper presents a Gaussian-Poisson mixture model (GPMM) which can reflect a frequency of event occurrence, for detecting anomaly of crowd behaviours. GPMM exploits the complementary information of both a statistics of crowd behaviour patterns and a count of the observed behaviour, and we learn the statistics of normal crowd behaviours for behaviours that occur frequently in the past by placing different weights, depending on the frequency occur. GPMM implicitly accounts for the motion patterns and the count of occurrence. The dense optical flow and an interactive force are used to represent a scene. We demonstrate the proposed method on a publicly available dataset, and the experimental results show that the proposed method could achieves competitive performances with respect to state-of-the-art approaches.