Automatic human behaviour anomaly detection in surveillance video

Abstract

This thesis work focusses upon developing the capability to automatically evaluate and detect anomalies in human behaviour from surveillance video. We work with static monocular cameras in crowded urban surveillance scenarios, particularly air- ports and commercial shopping areas. Typically a person is 100 to 200 pixels high in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo- ple at any given time. Our procedure evaluates human behaviour unobtrusively to determine outlying behavioural events, agging abnormal events to the operator. In order to achieve automatic human behaviour anomaly detection we address the challenge of interpreting behaviour within the context of the social and physical environment. We develop and evaluate a process for measuring social connectivity between individuals in a scene using motion and visual attention features. To do this we use mutual information and Euclidean distance to build a social similarity matrix which encodes the social connection strength between any two individuals. We de- velop a second contextual basis which acts by segmenting a surveillance environment into behaviourally homogeneous subregions which represent high tra c slow regions and queuing areas. We model the heterogeneous scene in homogeneous subgroups using both contextual elements. We bring the social contextual information, the scene context, the motion, and visual attention features together to demonstrate a novel human behaviour anomaly detection process which nds outlier behaviour from a short sequence of video. The method, Nearest Neighbour Ranked Outlier Clusters (NN-RCO), is based upon modelling behaviour as a time independent se- quence of behaviour events, can be trained in advance or set upon a single sequence. We nd that in a crowded scene the application of Mutual Information-based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in all the datasets we test upon. We additionally demonstrate that our work is applicable to other data domains. We demonstrate upon the Automatic Identi cation Signal data in the maritime domain. Our work is capable of identifying abnormal shipping behaviour using joint motion dependency as analogous for social connectivity, and similarly segmenting the shipping environment into homogeneous regions.