Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Abstract

The research of pedestrian target detection in complex scenes is still of great significance. Aiming at the problem of high missed detection rate and poor timeliness of pedestrian target detection in complex scenes. This paper proposes an improved classification method. First, Haar features were extracted from the images to be detected, and the candidate areas of pedestrians were determined by Adaboost classifier. Then, the traditional SVM classifier was improved by using the combined kernel function instead of the single kernel function, and the optimal proportion of each function in the combined kernel function was found by using the adaptive particle swarm optimization algorithm. Finally, the improved SVM classifier was combined with the fusion feature to further detect the candidate area to accurately locate the pedestrian’s position. Experimental results show that compared with the traditional detection framework, the proposed method can effectively improve the detection speed and the detection accuracy. This method has certain practical significance for pedestrian target detection in complex scenes.