基于数据增强和多层反向传播网络的行人重识别研究  

Pedestrian Re-Identification Research Based On Data Enhancement and Multiple Layer Back Propagation Network

作　　者：罗锋 LUO Feng(Aviation Maintenance Sergeant School,PLA Air Force Engineering University,Xinyang 464000,China)

机构地区：[1]中国人民解放军空军工程大学航空机务士官学校,河南信阳464000

出　　处：《现代信息科技》2021年第13期170-172,共3页Modern Information Technology

摘　　要：行人重识别在模式识别中占据很大的比重,这项技术的目的是识别出不同摄像机在不同时间且处于不同环境下的行人是否为相同身份。为了更好地表达行人特征,提出了一种数据增强和多层反向传播网络的方法(DAML)。文章认为样本图像类型的稀少和深度网络传播过程中数据丢失是导致识别率低的重要因素。我们希望增加图像样本数量,让深度网络中的每一层都进行回传,以提高识别率。在Market-1501、CUHK03和DukeMTMC-reID等主流数据集上,我们的方法取得了较好的效果。Pedestrian re-identification occupies a large proportion in pattern recognition.The purpose of this technology is to identify whether pedestrians under different cameras at different times and in different environments have the same identity.In order to express the characteristics of pedestrians better,a data augmentation and multiple layer back propagation network method(DAML)is proposed.It is considered that the scarcity of sample image types and data loss in the process of deep network propagation are important factors leading to low recognition rate.We hope to increase the number of image samples and let each layer in the deep network pass back to improve the recognition rate.Our method has achieved good effects on mainstream data sets such as Market-1501,CUHK03 and DukeMTMC-reID.

关 键 词：行人重识别 数据增强 多层反向传播 特征 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]