A Fast Detection and Recognition Algorithm for Pedestrian at Night Based on Entropy Weight Fast Support Vector Machine

Abstract

In allusion to such problems as real-time requirement dissatisfaction and significant recognition difference caused by dimension difference existing in the imaging and recognition algorithm for pedestrian in dark scene, a fast head detection and recognition method for pedestrian at night based on fast support vector machine (FC-SVM) algorithm optimization and entropy weight is established in this paper according to relevant principle of statistics. Based on entropy weight, this method aims at improving the extraction process based on histogram gradient features in order to establish threebranch SVM for the deep recognition of pedestrian at night; meanwhile, FC-SVM algorithm is combined to optimize the recognition calculation overhead in order to ensure the real-time property of the recognition algorithm. Furthermore, the falsely detected pedestrians are evaluated on the basis of the head detection mode so as to improve pedestrian imaging matching accuracy. The simulation result shows that this method can not only effectively recognize FIR target of pedestrian at night, but also effectively adapt to such different application environments as urban and suburban areas on the basis of ensuring the real-time requirement for pedestrian recognition, thus presenting good practicability.