Front Vehicle Detection Algorithm in Multi-lane Complex Environment
Journal Title: 河南科技大学学报（自然科学版） - Year 2018, Vol 39, Issue 2
Abstract
In order to solve the problem of front vehicle target detection in intelligent vehicle auxiliary driving system, a vehicle detection algorithm based on improved shadow feature and depth network learning was proposed.Based on the safety distance between the front vehicle and the vehicle, the bottom of the road image was selected as the candidate road background and the interference was eliminated by preprocessing.Then the bottom vehicle shadows enhancement image was obtained through the difference.The adaptive threshold method was used to determine the gray scale threshold of the image and the morphological preprocessing of the roadbinarized image was carried out.Then, the candidate vehicle target was selected by the minimum circumscribed rectangular box.The vehicle hypothesis was generated by combining the geometric characteristics of the vehicle shadow and the symmetry characteristics of the vehicle. Finally, the vehicle hypothesis was verified based on local binary pattern ( LBP) texture feature and depth learning method. The experimental results show that the algorithm can effectively detect front vehicle target in the multi-lane environment with complex interference.
Authors and Affiliations
Dong KONG, Jiangliang HUANG, Liang SUN, Zhiwei ZHONG, Yifan SUN
Keywords
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