The AIT Outdoors Tracking System for Pedestrians and Vehicles

摘要

This paper presents the tracking system from Athens Information Technology that participated to the pedestrian and vehicle surveillance task of the CLEAR 2006 evaluations. Two are the novelties of the proposed tracker. First, we use a variation of Stauffer's adaptive background algorithm with spatiotemporal adaptation of the learning parameters and a Kalman filter in a feedback configuration. In the feed-forward path, the adaptive background module provides target evidence to the Kalman filter. In the feedback path, the Kalman filter adapts the learning parameters of the adaptive background module. Second, we combine a temporal persistence pixel map, together with edge information, to produce the evidence that is associated with targets. The proposed tracker performed well in the evaluations, and can be also applied to indoors settings and multi-camera tracking.