Anomaly detection and prediction of human actions in a video surveillance environment

Abstract

World wide focus has over the years been shifting towards security issues, not in least due to recent world wide terrorist activities. Several researchers have proposed state of the art surveillance systems to help with some of the security issues with varying success. Recent studies have suggested that the ability of these surveillance systems to learn common environment behaviour patterns as well as to detect and predict unusual, or anomalous, activities based on those learnt patterns are possible improvements to those systems. I addition, some of these surveillance systems are still run by human operators, who are prone to mistakes and may need some help from the surveillance systems themselves in detection of anomalous activities. This dissertation attempts to address these suggestions by combining the fields of image understanding and artificial intelligence, specifically Bayesian Networks, to develop a prototype video surveillance system that can learn common environmental behaviour patterns, thus being able to detect and predict anomalous activity in the environment based on those learnt patterns. In addition, this dissertatio aims to show how the prototpe system can adapt to these anomalous behaviours and integrate them into its common patterns over a prolonged occurrence period.