A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes

Abstract

This paper presents a variety of probabilistic models for tracking small-area targets which are common objects of interest in outdoor visual surveillance scenes. We address the problem of using appearance and motion models in classifying and tracking objects when detailed information of the object's appearance is not available. The approach relies upon motion, shape cues and colour information to help in associating objects temporally within a video stream. Unlike previous applications of colour and complex shape in object tracking, where relatively large-size targets are tracked, our method is designed to track small colour targets commonly found in outdoor visual surveillance. Our approach uses a robust background model based around online Expectation Maximisation to segment moving objects with very low false detection rates. The system also incorporates a shadow detection algorithm which helps alleviate standard environmental problems associated with such approaches. A colour transformation derived from anthropological studies to model colour distributions of low-resolution targets is used along with a probabilistic method of combining colour and motion information. A data association algorithm is applied to maintain tracking of multiple objects under circumstances. Simple shape information is employed to detect subtle interactions such as occlusion and camouflage. A novel guided search algorithm is then introduced to facilitate tracking of multiple objects during these events. This provides a robust visual tracking system which is capable of performing accurately and consistently within a real world visual surveillance arena. This paper shows the system successfully tracking multiple people moving independently and the ability of the approach to maintain trajectories in the presence of occlusions and background clutter.