doi:10.16186/j.cnki.1673-9787.2020080056

Received:2020/08/22

Revised:2020/11/09

Published:2022/05/15

Preceding vehicle detection based on multi-feature fusion and information entropy optimization

ZHANG Zheng, WANG Sunqiang, HU Xinyu, XIONG Shenghui, HU Linghui

School of Mechanical Engineering， Hubei University of Technology， Wuhan 430068 ， Hubei， China

Abstract:Aiming at the problem that using a single feature was easily affected by environmental factors such as illumination and weather in the preceding vehicle detection on the expressways, a vehicle detection algorithm based on multi-feature fusion and optimization was proposed. Firstly, several vehicle hypothesis regions were generated by using an adaptive Otsu algorithm to segment the road surface. Then ,on the basis of histogram of gradient( HOG ) features, geometric features, texture features and amplitude features were introduced to build the features vectors which were optimized according to the information entropy. Finally, the support vector machine(SVM) classifier was trained to verify the hypothetical region The experimental results showed that the proposed algorithm improved the accuracy of the preceding vehicle detection and expanded the applicable scope of the preceding vehicle detection.

Key words:preceding vehicle detection;SVM;multi-feature fusion;feature optimization;information entropy optimization

基于多特征融合和信息熵优化的前车检测_张铮.pdf