基于计算机视觉的行人和车道检测

R. Srija, K. Kumar
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引用次数: 0

摘要

印度的公路网超过5231922公里。2015年,全国共发生交通事故496762起,受伤466567人,死亡177423人。为了避免这类交通事故,应该防止行人与车辆在高速公路上的碰撞。为此,提出了一种基于深度学习和计算机视觉技术的高速公路车道和行人自动检测方法,以防止道路交通事故的发生。行人检测使用tensorflow和HOG检测器完成。车道检测确保车辆在车道约束范围内,避免与附近车道上的车辆发生碰撞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pedestrian and Lane Detection using Computer Vision
: The road network of India is over 52,31,922 kms. During 2015 , the total number of traffic accidents were 4,96,762 with 4,86,567 persons were injured and 1,77,423 were killed.In order to avoid such road accidents, the collision of pedestrian with the vehicle on the highway should be prevented. So an approach for automatic lane and pedestrian detection on highways to prevent road accidents using deep learning and computer vision techniques is proposed. The pedestrian detection is done using tensorflow and HOG detector. Lane detection ensures that the vehicles are within the lane constraints and avoids the collision with vehicles on the nearby lanes.
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