  Detecting 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. In the recent decade, video anomaly
detection (VAD) techniques have achieved remarkable success in detecting
individual-level abnormal behaviors (e.g., sudden running, fighting and
stealing), but research on VAD for CABs is rather limited. 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. In this paper, we present a systematic study to
tackle the important problem of VAD for CABs with a novel crowd motion learning
framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first
captures the spatial and temporal crowd motion consistency information in a
graph representation. Then, it simultaneously trains multiple feature graphs
constructed at different scales to capture rich crowd patterns. An attention
network is used to adaptively fuse the multi-scale features for better CAB
detection. For the empirical study, we consider three large-scale crowd event
datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net
could substantially improve the state-of-the-art performance on all the
datasets.
