Improving the performance of traffic sign detection using blob tracking

Abstract

Abstract:We propose a new approach for tracking circular traffic signs from image sequences to improve the performance of traffic sign detection, by reducing search region and suppressing misdetection caused by temporal occlusion or poor quality of image. Our proposed tracking, called two-layered blobs tracking, does not require an accurate model of the fixed object-moving camera system, which is essential in the Kalman-Filter tracking. The experimental results show that the proposed approach could track the circular traffic signs from a moving camera effectively, without any restrictions on speed and movement of the vehicle, and camera installation, thus it is easy to be implemented in real situation. Keywords: traffic sign detection, traffic sign tracking, two-layer blobs, blob tracking, ring-partitioned matching Classification: Science and engineering for electronics References[1] Y. Aoyagi and T. Asakura, "A study on traffic sign recognition in scene image using genetic algorithms and neural networks," Proc.

Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

“…By using this method, image data is simplified for the purpose of minimizing the amount of data to be processed. This method was used in [63,64,65,66,67] for indicating the boundaries of objects within the image through finding a set of connected curves. The Haar-like features method was proposed by Paul Viola and Michael Jones [68] based on the Haar wavelet to recognize the target objects.…”