中南民族大学学报（自然科学版）2018，Vol.37Issue(1)：108-113,6.
基于自编码神经网络与AdaBoost的快速行人检测算法
Fast Pedestrian Detection Algorithm Based on Auto-encoder Neural Network and AdaBoost
摘要
Abstract
Since the traditional algorithm has the shortages of slow detection rate and large error rate in pedestrian detection,a fast pedestrian detection algorithm based on auto-encoder neural network and AdaBoost is proposed. Firstly, the pedestrian detection algorithm based on ACF model is used to process the image to obtain the suspected object area. Then the acquired sub-region is normalized and the HOG feature is extracted and input into the auto-encoder neural network. Finally,the AdaBoost classifier is used to detect the classification and output the detected pedestrian area. The experimental results show that the proposed method has more performance than the existing detection algorithm for pedestrian detection,and its detection speed is also faster than most of the algorithms.关键词
行人检测/HOG特征/AdaBoost算法/自编码网络/ACF模型Key words
pedestrian detection/HOG feature/AdaBoost algorithm/auto-encoder network/ACF model分类
信息技术与安全科学引用本文复制引用
韩宪忠,李得锋,王克俭,周利亚..基于自编码神经网络与AdaBoost的快速行人检测算法[J].中南民族大学学报（自然科学版）,2018,37(1):108-113,6.基金项目
河北省科技项目基金资助项目(14227404D) （14227404D）
河北农业大学理工基金项目(LG201407 （）
ZD201407 （）
LG20140703) （）
河北省高等学校科学技术研究项目(ZD2015054) （ZD2015054）
