{"title":"基于视频序列的人体运动预测","authors":"Zhuoheng Huang, Yue Yu, Xiangru Chen, Wei Wei","doi":"10.1109/ICCT.2018.8600154","DOIUrl":null,"url":null,"abstract":"Learning to predict human body motion has emerged as a meaningful research in computer vision and artificial intelligence. This paper presents the study on predicting human body motion from video sequences. We propose a human body motion prediction network integrating the recent advanced 2D feature extraction and video sequences prediction. Based on the temporal characteristics extracted from video sequences, our network realizes the prediction of the human motion. We train the network using the video based human pose datasets and demonstrate good performance of our network on 2D human body motion prediction through quantitative and qualitative results. Experimental results prove the feasibility of our method.","PeriodicalId":244952,"journal":{"name":"2018 IEEE 18th International Conference on Communication Technology (ICCT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Human Body Motion from Video Sequences\",\"authors\":\"Zhuoheng Huang, Yue Yu, Xiangru Chen, Wei Wei\",\"doi\":\"10.1109/ICCT.2018.8600154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Learning to predict human body motion has emerged as a meaningful research in computer vision and artificial intelligence. This paper presents the study on predicting human body motion from video sequences. We propose a human body motion prediction network integrating the recent advanced 2D feature extraction and video sequences prediction. Based on the temporal characteristics extracted from video sequences, our network realizes the prediction of the human motion. We train the network using the video based human pose datasets and demonstrate good performance of our network on 2D human body motion prediction through quantitative and qualitative results. Experimental results prove the feasibility of our method.\",\"PeriodicalId\":244952,\"journal\":{\"name\":\"2018 IEEE 18th International Conference on Communication Technology (ICCT)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 18th International Conference on Communication Technology (ICCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCT.2018.8600154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 18th International Conference on Communication Technology (ICCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCT.2018.8600154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Human Body Motion from Video Sequences
Learning to predict human body motion has emerged as a meaningful research in computer vision and artificial intelligence. This paper presents the study on predicting human body motion from video sequences. We propose a human body motion prediction network integrating the recent advanced 2D feature extraction and video sequences prediction. Based on the temporal characteristics extracted from video sequences, our network realizes the prediction of the human motion. We train the network using the video based human pose datasets and demonstrate good performance of our network on 2D human body motion prediction through quantitative and qualitative results. Experimental results prove the feasibility of our method.