Detecting and Quantifying Crowd-Level Abnormal Behaviors in Crowd Events

Detecting and quantifying abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally and the scale of CABs could vary from one scenario to another. It is also challenging to quantify the risk level of these CABs from video surveillance. In this paper, we present an improved version of our crowd motion learning framework for CABs detection, multi-scale motion consistency network (MSMC-Net) with a dual-attention fusion process to accommodate both the spatio-temporal and scale variations of different CABs. In addition, we propose an assessment method to quantify the risk level of detected CABs based on the anomaly score generated from our MSMC-Net. The risk quantification is performed in an online and accumulated manner and it can reflect the risk level of CABs consistent with other offline assessment metrics (e.g., crowd pressure), but without the extraction of detailed crowd data (e.g., pedestrian trajectories). For empirical study, we evaluate our method on large-scale crowd event datasets, including UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could improve the AUC performance by 7.9%, 12.2% and 29.5% on three datasets respectively, compared to the best results of the state-of-the-art methods.