Real-time Road Obstacle Detection Using Association and Symmetry Recognition

This paper presents a fast road obstacle detection system based on association and symmetry. This approach consists to exploit the edges extracted from consecutive images acquired by a stereo sensor embedded in a moving vehicle. The algorithm contains three main components: edges detection, association detection and symmetry calculation. The edges detection is achieved by using the canny operator and point corner to extract all possible edges of different objects at the image. The association technique is used to exploit relationship between the edges of two consecutives images by combining it with the moment operator. The symmetry is used as road obstacle validation; the road obstacles like vehicle and pedestrian have a vertical symmetry. The proposed approach has been tested on different images. The provided results demonstrate the effectiveness of the proposed method

Stereo Vision-Based Road Obstacles Detection and Tracking

This paper presents a fast road obstacle detection system based on stereo vision. The algorithm contains three main components: road detection, obstacle detection and vehicle tracking. The road detection is achieved by using a small rectangular shape at bottom center of disparity image to extract the disparities of the road. The roadsides are located by using morphological processing and Hough transform. In the obstacle detection process, the objects can be easily located by the segmentation process. The vehicle tracking is achieved by the discrete Kalman filter. The proposed approach has been tested on different images. The provided results demonstrate the effectiveness of the proposed method