Maintaining trajectories of salient objects for robust visual tracking

Abstract

This paper presents a robust approach to track multiple objects for low resolution, far-field visual surveillance applications. Multiple moving objects are detected by utilizing an adaptive background model and tracked by resolving the correspondence between their trajectory segments using proximity and appearance similarity measures. A new confidence measure is assigned to each possible match between objects and this information is maintained by a graph structure. This graph is utilized to prune and refine the trajectories. Kalman filter is used to handle discontinuities and occlusions. Proposed approach handles problems such as spurious objects, fragmentation, shadow, clutter and occlusions. © Springer-Verlag Berlin Heidelberg 2005.