Pedestrian Detection Combining with SVM Classifier and HOG Feature Extraction
摘要
Aiming at the problem that pedestrian detection scheme based on Histogram of Oriented Gradient(HOG) has large computation and poor real-time,this paper designs a HOG feature extraction normalized module embedded Support Vector Machine (SVM) classifier,and applies it to pedestrian detection.It proposes a two-stage pipeline architecture.On the first level,it uses 16×16 pixel scanning,simplifies the histogram generation with Look-up Table(LUT),and it can reduce resources consumption of multiplier.On the second level,the 15-way parallel SVM is embedded itself in the HOG normalization module,and it can reduce bit of 15-way parallel SVM multiply-accumulator through pre-start SVM.Also,an algorithm is proposed to automatically reduce duplicated detection to improve detection accuracy.The scheme is verified for SVGA resolution video(800×600) at 47 frames on Spartan6 Field Programmable Gate Array(FPGA) with 100 MHz and it improves the real-time and accuracy of pedestrian detection.