This paper describes an efficient method for the detection of triangular traffic signs on grey-scale images. This method is based on the proposed RSLD (RANSAC Symmetric Lines Detection) algorithm which transforms triangle detection into a simple segment detection. A multi-scale approach allows the detection of any warning and yield traffic signs, whatever their distance to the vehicle. This algorithm is applied to a set of selected corners obtained with a coding gradient method. Baseline detection uses the scale of selected triangles to confirm the presence of traffic signs. The study demonstrates that RSLD is a low computation method compared to standard triangle detection. The performance of the method proposed is compared with recently published methods on road sign databases, which use colour information. An equivalent detection rate is obtained with this algorithm, working on grey-scale images. This algorithm is implemented and runs in realtime at 30 frames per second.

This paper presents an object tracking algorithm using belief functions applied to vision-based traffic sign recognition systems. This algorithm tracks detected sign candidates over time in order to reduce false positives due to data fusion formalization. In the first stage, regions of interest (ROIs) are detected and combined using the transferable belief model semantics. In the second stage, the local pignistic probability algorithm generates the associations maximizing the belief of each pairing between detected ROIs and ROIs tracked by multiple Kalman filters. Finally, the tracks are analyzed to detect false positives. Due to a feedback loop between the multi-ROI tracker and the ROI detector, the solution proposed reduces false positives by up to 45%, whereas computation time remains very low.

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.

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