Performance Evaluation of a People Tracking System on PETS2009 Database

In this paper a system for autonomous video surveillance in relatively unconstrained environments is described. The system consists of two principal phases: object detection and object tracking. An adaptive background subtraction, together with a set of corrective algorithms, is used to cope with variable lighting, dynamic and articulate scenes, etc. The tracking algorithm is based on a matrix representation of the problem, and is used to face splitting and occlusion problems. When the tracking algorithm fails in following actual object trajectories, an appearance-based module is used to restore object identities. An experimental evaluation, carried out on the PETS2009 dataset for tracking, shows promising results.