Coupled detection, association and tracking for Traffic Sign Recognition

Abstract

This paper tackles the problem of tracking-based Traffic Sign Recognition (TSR) systems. It presents an integrated object detection, association and tracking approach based on a spatio-temporal data fusion. This algorithm tracks detected sign candidates in order to reduce false positives. Regions Of Interest (ROIs) potentially containing traffic signs are determined from the vehicle-mounted camera images. An original corner detector associated to pixel coding ensures the detection efficiency. The ROIs are combined using the Transferable Belief Model semantics. The associations maximizing the pairwise belief between the detected ROIs and ROIs tracked by multiple Kalman filters are processed. The track evolution helps to detect false positives. Thanks to this solution and to a feedback loop between the tracking algorithm and the ROI detector, a false positive reduction of 45% is assessed.