Two-stage Pedestrian Detection Based on Multiple Features and Machine Learning
一种基于多特征和机器学习的分级行人检测方法

A two-stage detection method based on Adaboost and support vector machine (SVM) is proposed for the pedestrian detection problem in a single image, which uses the combination of coarse level and fine level detection to improve the accuracy of the detector. The coarse level pedestrian detector makes use of the four direction features (FDF) and the gentle Adaboost (GAB) cascade training; the fine level pedestrian detector uses entropy-histograms of oriented gradients (EHOG) as features and the SVM as classifier. The proposed EHOG features considering entropy and the distribution of chaos have the ability to distinguish between the pedestrians and the objects similar to people. Experimental results show that the proposed two-stage pedestrian detection method with the combination of the coarse-fine level and EHOG feature can accurately detect upright bodies with different postures in the complex background, at the same time the precision is better than the classic Adaboost methods.