Traffic sign detection via interest region extraction. Issue 4 (April 2015)

• Record Type: Journal Article
• Authors: Salti, Samuele; Petrelli, Alioscia; Tombari, Federico; Fioraio, Nicola; Di Stefano, Luigi
• Is Part Of: Pattern recognition. Volume 48:Issue 4(2015:Apr.)
• Page Start: 1039
• Page End: 1049
• Publication Date: 2015-04
• Subjects: Mobile mapping -- Traffic sign detection -- Local features; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.05.017
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: Mobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of mandatory, prohibitory and danger traffic signs, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data. Abstract : Highlights: The paper presents in detail the design of a Traffic Sign Detection pipeline. Interest regions are an effective tool to feed a Traffic Sign Detection pipeline. A context-aware and a traffic light filter can effectively prune false positives. Our algorithm obtains competitive results on a public benchmark dataset. Our pipeline achieves promising results on a challenging mobile mapping dataset.