Self-Organizing Activity Description Map to represent and classify human behaviour

Abstract

The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts in recent years. This paper proposes the Self Organizing Activity Description Map (SOADM). It is a novel neural network based on the self-organizing paradigm to classify high level of semantic understanding from video sequences. The neural network is able to deal with the big gap between human trajectories in a scene and the global behaviour associated to them. Specifically, using simple representations of people trajectories as input, the SOADM is able to both represent and classify human behaviours. Additionally, the map is able to preserve the topological information about the scene. Experiments have been carried out using the Shopping Centre dataset of the CAVIAR database taken into account the global behaviour of an individual. Results confirm the high accuracy of the proposal outperforming previous methods.