Chapter
Jun 17, 2014
Video-Based Detection and Classification of US Traffic Signs and Mile Markers using Color Candidate Extraction and Feature-Based Recognition
Authors: Vahid Balali [email protected] and Mani Golparvar-Fard [email protected]Author Affiliations
Publication: Computing in Civil and Building Engineering (2014)
Abstract
Traffic sign and mile-marker detection and classification are among the important components of highway asset management systems. The significant number of these high-quantity and low-cost assets in US highways can negatively impact the quality of any manual data collection and analysis. To address these challenges, this paper presents an efficient pipeline for video-based detection and classification of traffic signs and mile-markers based on color and shape criteria. Candidate extraction is based on finding the optimum RGB thresholds which yield high detection rates (very low false-negatives) while keeping the number of false-positives in check. The connected components from a thresholded image are extracted next. We use sliding windows, Haar-like features, and the AdaBoost learning method to classify the detected assets. Experimental results with an average classification accuracy of 79.30% on actual data collected from US-460 highway show the promise of the proposed method for reducing the time and effort required for developing traffic road asset inventories.
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© 2014 American Society of Civil Engineers.
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Published online: Jun 17, 2014
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PhD Candidate, Departments of Civil and Environmental Engrg., Univ. of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801;. E-mail: [email protected]
Assistant Professor and NCSA Faculty Fellow, Departments of Civil and Environmental Engrg. and Computer Science, Univ. of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.