Detecting Abnormal Behaviors in Crowded Scenes

Situational awareness is a basic function of the human visual system, which is attracting a lot of research attention in machine vision and related research communities. There is an increasing demand for smarter video surveillance of public and private space using intelligent vision systems which can distinguish what is semantically meaningful to the human observer as ‘normal’ and ‘abnormal’ behaviors. In this study we propose a novel robust behavior descriptor for encoding the intrinsic local and global behavior signatures in crowded scenes. Crowd scenes transitioning from normal to abnormal behaviors such as “rush”, “scatter” and “herding” were modeled and detected. The descriptor uses features that encode both local and global signatures of crowd interactions. Bayesian topic modeling is used to capture the intrinsic structure of atomic activity in the video frames and used to detect the transition from normal to abnormal behavior. Experimental results and analysis of the proposed framework on two publicly available crowd behavior datasets show the effectiveness of this method compared to other methods for anomaly detection in crowds with a very good detection accuracy rates.