Research On Pedestrian Detection Method Based On Vehicle Video

Traffic accidents can cause serious harm to human safety,so how to avoid traffic accidents are the focus of attention.Vehicle assisted driving technology is an important method to avoid traffic accidents,and pedestrian detection technology is the important point of vehicle assisted driving technology.In order to improve the speed and accuracy of pedestrian detection,the thesis was mainly talking about follow aspects: Region of interest was extracted;Pedestrian features were extracted;The parameters of SVM classifier were optimized;Classifiers were trained and pedestrians were detected.First,a method of quickly extracting the region of interest was proposed,based on the principle of camera imaging and edge detection.Detection time can be reduced and detection speed can be improved by the method.Second,the pedestrian's HOG features and LBP features were extracted.Extracted HOG features were reduced in dimension through the PCA algorithm,and the HOG-LBP features were synthesized by cascading the features of dimension-reduced HOG and LBP.The HOG-LBP feature was the input factor of the classifier.Then,the parameters of the SVM were optimized by the artificial bee colony algorithm and the chaotic artificial bee colony algorithm.MATLAB simulations show that the classifier optimized by the chaotic artificial bee colony algorithm is superior to the artificial bee colony algorithm.The former have a higher classification accuracy.Finally,pedestrians were detected through the optimized SVM classifier.Optimized SVM classifier can quickly detect pedestrians,better than the unoptimized SVM classifier.