Classifying and tracking multiple persons for proactive surveillance of mass transport systems

Abstract

We describe a pedestrian classification and tracking system that is able to track and label multiple people in an outdoor environment such as a railway station. The features selected for appearance modelling are circular colour histograms for the hue and conventional colour histograms for the saturation and value components. We combine blob matching with a particle filter for tracking and augment these algorithms with colour appearance models to track multiple people in the presence of occlusion. In the object classification stage, hierarchical chamfer matching combined with particle filtering is applied to classify commuters in the railway station into several classes. Classes of interest include normal commuters, commuters with backpacks, commuters with suitcases, and mothers with their children.