A Context-Based Approach for Detecting Suspicious Behaviours

Abstract

A video surveillance system capable of detecting suspicious activities or behaviours is of paramount importance to law enforcement agencies. Such a system will not only reduce the work load of security personnel involved with monitoring the CCTV video feeds but also improve the time required to respond to any incident. There are two well known models to detect suspicious behaviour: misuse detection models which are dependent on suspicious behaviour definitions and anomaly detection models which measure deviations from defined normal behaviour. However, it is nearly possible to encapsulate the entire spectrum of either suspicious or normal behaviour. One of the ways to overcome this problem is by developing a system which learns in real time and adapts itself to behaviour which can be considered as common and normal or uncommon and suspicious. We present an approach utilising contextual information. Two contextual features, namely, type of behaviour and the commonality level of each type are extracted from long-term observation. Then, a data stream model which treats the incoming data as a continuous stream of information is used to extract these features. We further propose a clustering algorithm which works in conjunction with data stream model. Experiments and comparisons are conducted on the well known CAVIAR datasets to show the efficacy of utilising contextual information for detecting suspicious behaviour. The proposed approach is generic in nature and can be applicable to any features. However for the purpose of this study, we have employed pedestrian trajectories to represent the behaviour of people.