2014/01/09 by Olusanya Y. Agunbiade, Agunbiade, Olusanya Y., Tranos Zuva +5
Computer Science · Engineering · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics (cs.RO) #Video Surveillance and Tracking Methods #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.1401.2051
Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.6, December 2013
arxiv created 2014/01/09 · openalex publication_date 2014/01/09 · arxiv updated 2014/01/10 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Road region recognition is a main feature that is gaining increasing attention from intellectuals because it helps autonomous vehicle to achieve a successful navigation without accident. However, different techniques based on camera sensor have been used by various researchers and outstanding results have been achieved. Despite their success, environmental noise like shadow leads to inaccurate recognition of road region which eventually leads to accident for autonomous vehicle. In this research, we conducted an investigation on shadow and its effects, optimized the road region recognition system of autonomous vehicle by introducing an algorithm capable of detecting and eliminating the effects of shadow. The experimental performance of our system was tested and compared using the following schemes: Total Positive Rate (TPR), False Negative Rate (FNR), Total Negative Rate (TNR), Error Rate (ERR) and False Positive Rate (FPR). The performance result of the system improved on road recognition in shadow scenario and this advancement has added tremendously to successful navigation approaches for autonomous vehicle.