{"title":"使用激光雷达和深度学习技术的人类步态识别","authors":"Tzu-Chun Chiu, Tzung-Shi Chen, Jing-Mei Lin","doi":"10.1109/ICCE-Taiwan55306.2022.9869258","DOIUrl":null,"url":null,"abstract":"This paper presents a system using Light Detection and Ranging (LiDAR) to sense the human gait, and training several deep learning models for gait recognition through the collected point cloud. Since the behavior of the human body is a continuous action, we choose deep learning architectures which deal with time-series data, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and make the appropriate architecture combination to improve the accuracy of recognizing human gait.","PeriodicalId":164671,"journal":{"name":"2022 IEEE International Conference on Consumer Electronics - Taiwan","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Human Gait Recognition using LiDAR and Deep Learning Technologies\",\"authors\":\"Tzu-Chun Chiu, Tzung-Shi Chen, Jing-Mei Lin\",\"doi\":\"10.1109/ICCE-Taiwan55306.2022.9869258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a system using Light Detection and Ranging (LiDAR) to sense the human gait, and training several deep learning models for gait recognition through the collected point cloud. Since the behavior of the human body is a continuous action, we choose deep learning architectures which deal with time-series data, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and make the appropriate architecture combination to improve the accuracy of recognizing human gait.\",\"PeriodicalId\":164671,\"journal\":{\"name\":\"2022 IEEE International Conference on Consumer Electronics - Taiwan\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Consumer Electronics - Taiwan\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Consumer Electronics - Taiwan","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Human Gait Recognition using LiDAR and Deep Learning Technologies
This paper presents a system using Light Detection and Ranging (LiDAR) to sense the human gait, and training several deep learning models for gait recognition through the collected point cloud. Since the behavior of the human body is a continuous action, we choose deep learning architectures which deal with time-series data, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) and make the appropriate architecture combination to improve the accuracy of recognizing human gait.
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