Hazardous sign detection for safety applications in traffic monitoring

Wanda Benesova; Michal Kottman; Oliver Sidla

doi:10.1117/12.905813

23 January 2012 Hazardous sign detection for safety applications in traffic monitoring

Wanda Benesova, Michal Kottman, Oliver Sidla

Proceedings Volume 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques; 830109 (2012) https://doi.org/10.1117/12.905813
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

The transportation of hazardous goods in public streets systems can pose severe safety threats in case of accidents. One of the solutions for these problems is an automatic detection and registration of vehicles which are marked with dangerous goods signs. We present a prototype system which can detect a trained set of signs in high resolution images under real-world conditions. This paper compares two different methods for the detection: bag of visual words (BoW) procedure and our approach presented as pairs of visual words with Hough voting. The results of an extended series of experiments are provided in this paper. The experiments show that the size of visual vocabulary is crucial and can significantly affect the recognition success rate. Different code-book sizes have been evaluated for this detection task. The best result of the first method BoW was 67% successfully recognized hazardous signs, whereas the second method proposed in this paper - pairs of visual words and Hough voting - reached 94% of correctly detected signs. The experiments are designed to verify the usability of the two proposed approaches in a real-world scenario.

Citation Download Citation

Wanda Benesova, Michal Kottman, and Oliver Sidla "Hazardous sign detection for safety applications in traffic monitoring", Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 830109 (23 January 2012); https://doi.org/10.1117/12.905813

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KEYWORDS

Visualization

Safety

Sensors

Computer vision technology

Detection and tracking algorithms

Image resolution

Machine vision

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