Study on Nighttime Vehicle Detection Using Rear Features and Road Lane

• Main Authors: LOH, WAI-LEONG, 羅維良
• Other Authors: CHANG, JIE-REN
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/et2x2g

碩士 === 國立宜蘭大學 === 電子工程學系碩士班 === 106 === According to studies, on traffic roads the accident rate at night was higher than during day. In particular, human error is still the major cause of accidents and incidents. In order to solve the accidents caused by driving, the application of Advanced Driver Assistance Systems (ADAS) are becoming more important than ever. ADAS developments for vision systems are a key focus that detect vehicles on the road in front of one's own by using computer vision technologies. There are designed to warn of possible dangers and prevent traffic accidents. This paper is focused on taillight to develop a computer vision-based nighttime vehicle detection. We present a novel adaptive threshold technique based on fuzzy system and a dynamic threshold is calculated using Otsu's method. In order to ensure correctness of detecting, we also create a new detection model to determine the position of target vehicles in road region. We propose a method that can help to overcome problems such as the temporary loss of the target position caused by using traditional detection model. In testing phase, we propose a dynamic threshold and compare two other studies through different traffic scenarios for simulation. Experiment results indicate that, the proposed scheme which can significantly improves vehicle detection rates under complex illumination changes. It achieves a good result to extract foreground objects from sample nighttime traffic scenes. We also explore how to deformable detection model to detect vehicles. These method can be used to effectively reduce the miss rate of our vehicle detection.