Creating robust high-throughput traffic sign detectors using centre-surround HOG statistics

Abstract

In this paper, we detail a system for creating object detectors which meet the extreme demands of real-world traffic sign detection applications such as GPS map making and real-time in-car traffic sign detection. The resulting detectors are designed to detect and locate multiple traffic sign types in high-definition video (high throughput) from several cameras captured along thousands of kilometers of road with minimal false-positives and detection rates in excess of 99%. This allows for the accurate detection and location of traffic signs in geo-tagged video datasets of entire national road networks in reasonable time using only moderate computing infrastructure. A key to the success of the methods described in this paper is the use of extremely efficient classifier features. In this paper, we identify two obstacles to achieving the desired performance for all target traffic sign types, feature memory bandwidth requirements and feature discriminance. We introduce our use of centre-surround histogram of oriented gradient (HOG) statistics which greatly reduce the per-feature memory bandwidth requirements. Subsequently we extend our use of centre-surround HOG statistics to the color domain, raising the discriminant power of the final classifiers for more challenging sign types.