A vehicle recognition algorithm based on fusion feature and improved binary normalized gradient feature

摘要

A vehicle detection method based on the fast extraction of object-oriented candidate window and fused feature of HOG-LBP is proposed for the vehicle detection algorithms based on the single shape feature in the video monitoring of expressway may lead to mistaken inspection and the detection algorithm using the support vector machine (SVM) sliding window is quite time-consuming. Firstly, the vehicle candidate window is quickly extracted based on the binary normalized gradient feature and the background difference, then the histograms of oriented gradients (HOG) feature of the candidate window image and the local binary pattern (LBP) feature are calculated and the feature fusion is carried out, and finally the vehicle detection is taken combing with the SVM classifier. The experimental results show that the fusion of shape and texture features can effectively improve the performance of vehicle detection, and the detection speed of SVM can be raised about 8 times by fast extraction of the candidate window, which can meet the requirements of real time engineering.