Color transformation for improved traffic sign detection

I.M. Creusen, L. Hazelhoff, P.H.N. With, de

Research output: Contribution to conferencePoster

137 Downloads (Pure)

Abstract

This paper considers large scale traffic sign detection on a dataset consisting of high-resolution street-level panoramic photographs. Traffic signs are automatically detected and classified with a set of state-of-the-art algorithms. We introduce a color transformation to extend a Histogram of Oriented Gradients (HOG) based detection algorithm to further improve the performance. This transformation uses a specific set of reference colors that aligns with traffic sign characteristics, and measures the distance of each pixel to these reference colors. This results in an improved consistency on the gradients at the outer edge of the traffic sign. In an experiment with 33, 400 panoramic images, the number of misdetections decreased by 53.6% and 51.4% for red/blue circular signs, and by 19.6% and 28.4% for yellow speed bump signs, measured at a realistic detector operating point.
Original languageEnglish
Pages461-464
DOIs
Publication statusPublished - 2012
Event19th IEEE International Conference on Image Processing (ICIP 2012) - Lake Buena Vista, FL, United States
Duration: 30 Sep 20123 Oct 2012
Conference number: 19

Conference

Conference19th IEEE International Conference on Image Processing (ICIP 2012)
Abbreviated titleICIP 2012
CountryUnited States
CityLake Buena Vista, FL
Period30/09/123/10/12

Bibliographical note

Proceedings of the 19th International Conference on Image Processing (ICIP) 2012, 30 September - 3 October 2012, Orlando, Florida

Fingerprint Dive into the research topics of 'Color transformation for improved traffic sign detection'. Together they form a unique fingerprint.

Cite this