A Fast Road Obstacle Detection Using Association and Symmetry recognition
Khalid Zebbara, Mohamed El Ansari, Abdenbi Mazoul, Hassan Oudani · 2019
This paper presents a quick road obstacle detection system supported association and symmetry. This approach consists to exploit the edges extracted from consecutive images acquired by a stereo device embedded in an exceedingly moving vehicle. The formula contains 3 main elements: 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 projected approach has been tested on completely different images. The provided results demonstrate the effectiveness of the proposed method.