Detection, Tracking and Recognition of Traffic Signs from Video Input

In this paper a comprehensive approach to the recognition of traffic signs from video input is proposed. A trained attentive classifier cascade is used to scan the scene in order to quickly establish regions of interest (ROI). Sign candidates within ROIs are captured by detecting the instances of equiangular polygons using a Hough Transform-style shape detector. To ensure a stable tracking of the likely traffic signs, especially in cluttered background, we propose a Pixel Relevance Model, where the pixel relevance is defined as a confidence measure for a pixel being part of a sign's contour. The relevance of the hypothesized contour pixels is updated dynamically within a small search region maintained by a Kalman filter, which ensures faster computation. Gradient magnitude is used as an observable evidence for this update process. In the classification stage, a temporally integrated template matching technique based on the class-specific discriminative local region representation of an image is adopted. We have evaluated the proposed approach on a large database of 135 traffic signs and numerous real traffic video sequences. A recognition accuracy of over 93% in near real-time has been achieved.