Efficient hybrid monocular-stereo approach to on-board video-based traffic sign detection and tracking

Abstract

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion.

Three Dimensional Tracking of Road Signs based on Stereo Vision Technique

“…또 다른 방법으로 도로 표지판이 3차원으로 공간에서 평면이라는 특징을 이용한다 [6]. Marinas [7,8]는 평면 정보를 바탕으로 칼만 필터를 사용하여 도로 표지판을 추적한다. 최근 연구들 에서 다양한 방법들이 도로 표지판 검출과 인식을 보다 정 확하고 강건하게 연구되고 있다 [9,10].…”