Zhang Yujie, Cai Lecai, Zhiming Wu, Kui Cheng, Di Wu, Keyuan Tang
{"title":"Research on gait recognition algorithm based on deep learning","authors":"Zhang Yujie, Cai Lecai, Zhiming Wu, Kui Cheng, Di Wu, Keyuan Tang","doi":"10.1109/ICCEAI52939.2021.00080","DOIUrl":null,"url":null,"abstract":"The accuracy of gait recognition method would be affected by the occlusion of clothing object being carried. To overcome the problem, this paper adopted the method based on CNN(Convolutional neural network) and LSTM(Long and short term memory network) to build gait recognition models. Specifically, CNN is used to extract the spatial features of pedestrians in training videos, and the LSTM network is used to extract the temporal and spatial features of gait video sequences. We optimize the LSTM network structure and parameters of the gait recognition models and compare the establish models with the models built in other research. The results show that the models establish in our research perform better that the models in other research.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEAI52939.2021.00080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The accuracy of gait recognition method would be affected by the occlusion of clothing object being carried. To overcome the problem, this paper adopted the method based on CNN(Convolutional neural network) and LSTM(Long and short term memory network) to build gait recognition models. Specifically, CNN is used to extract the spatial features of pedestrians in training videos, and the LSTM network is used to extract the temporal and spatial features of gait video sequences. We optimize the LSTM network structure and parameters of the gait recognition models and compare the establish models with the models built in other research. The results show that the models establish in our research perform better that the models in other research.