Performance Evaluation of a People Tracking System on PETS2009 Database

Abstract

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 appearancebased module is used to restore object identities. An experimental evaluation, carried out on the PETS2009 dataset for tracking, shows promising results.