Feature Fusion for Robust Object Tracking Using Fragmented Particles

Tracking people or objects across multiple cameras and maintaining a track within a camera is a challenging task in applications such as video surveillance. Some of the major challenges while tracking a target are illumination/scale changes and partial occlusion. In this paper, we propose a novel tracking framework using particle filter to efficiently track an object within a camera and a blob-based target association scheme for tracking across cameras. The proposed particle filter tracking algorithm uses a fragment-based approach to model the target and track it by fusing color and gradient features. Also, the proposed solution incorporates coarser level spatial information by fragmenting each particle and is shown to be beneficial for tracking under partial occlusion. A fast yet robust model update is employed to overcome illumination changes. Experimental results show (i) the robustness of the fragment-based tracking approach with respect to illumination/scale change and partial occlusion and (ii) tracking persons across two cameras.