Sub-regional and Multi-classifier Vehicle Detection Based on Haar-like and MB-LBP Features
ZHU Bin1,2, WANG Shaoping2, LIANG Huawei1,2, YUAN Sheng2, YANG Jing3, HUANG Junjie1,2
1.Department of Automation, University of Science and Technology of China, Hefei 230088 2.Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230088 3.Bionic Visual Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031
Abstract：The pre-collision system in the advanced vehicle-assisted driving system serves to detect both the forward vehicle to prevent the rear-end collision and the adjacent lane oblique lateral vehicle to realize the forecast of its potential lane change and confluence behavior, providing real-time warning function. In this paper, a method of vehicle detection with multiple characteristics of sub-region fusion is proposed to solve the obvious differences between forward vehicle and oblique lateral vehicle. The proposed method is utilized to detect the vehicles at different distances, respectively, to conduct image downsampling with different degrees. Consequently, the real-time performance of the detection system is improved. The field test of various traffic scenes indicates that the proposed method can detect the forward vehicle and the oblique side vehicle stably and accurately in real time. In good driving environment, the proposed method can achieve high recall rate and accuracy rate.
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2017, Vol. 30 
