计算机应用与软件2016，Vol.33Issue(5)：148-153,6.DOI:10.3969/j.issn.1000-386x.2016.05.037
一种用于行人检测的隐式训练卷积神经网络模型
A LATENT TRAINING MODEL OF CONVOLUTIONAL NEURAL NETWORKS FOR PEDESTRIAN DETECTION
摘要
Abstract
Pedestrian detection has become one of the hot research topics in various social fields.Convolutional neural networks have excellent learning ability.The characteristics of targets learned by these networks are more natural and more conducive to distinguishing different targets.However,traditional convolutional neural network models have to process entire target.Meanwhile,all the training samples need to be pre-labelled correctly,these hamper the development of convolutional neural network models.In this paper,we propose a convolutional neural network-based latent training model.The model reduces the computation complexity by integrating multiple part detection modules and learns the targets classification rules from unlabelled samples by adopting a latent training method.In the paper we also propose a two-stage learning scheme to overlay the size of the network step by step.Evaluation of the tests on public static pedestrian detection dataset,INRIA Person Dataset[1],demonstrates that our model achieves 98% of detection accuracy and 95% of average precision.关键词
行人检测/隐式训练/部件检测/卷积神经网络Key words
Pedestrian detection/Latent training/Part detection/Convolutional neural networks分类
信息技术与安全科学引用本文复制引用
黄咨,刘琦,陈致远,赵宇明..一种用于行人检测的隐式训练卷积神经网络模型[J].计算机应用与软件,2016,33(5):148-153,6.基金项目
国家自然科学基金项目（61175009）；上海市产学研合作项目（沪CXY－2013－82）。 （）
