{"title":"在智能家居中使用微软Kinect进行异常事件检测","authors":"Hsiu-Yu Lin, Yu-Ling Hsueh, W. Lie","doi":"10.1109/ICS.2016.0064","DOIUrl":null,"url":null,"abstract":"In this paper, we present a continuous deep learning model for fall detection using Microsoft Kinect. The input include pre-processed high-resolution RGB images, depth images collected by a Kinect and optical flow images. We combine several deep learning structures including convolutional neural networks and long short-term memory networks for continuous human fallen detection. Finally, we present experimental results to demonstrate the performance and utility of our approach.","PeriodicalId":281088,"journal":{"name":"2016 International Computer Symposium (ICS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Abnormal Event Detection Using Microsoft Kinect in a Smart Home\",\"authors\":\"Hsiu-Yu Lin, Yu-Ling Hsueh, W. Lie\",\"doi\":\"10.1109/ICS.2016.0064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a continuous deep learning model for fall detection using Microsoft Kinect. The input include pre-processed high-resolution RGB images, depth images collected by a Kinect and optical flow images. We combine several deep learning structures including convolutional neural networks and long short-term memory networks for continuous human fallen detection. Finally, we present experimental results to demonstrate the performance and utility of our approach.\",\"PeriodicalId\":281088,\"journal\":{\"name\":\"2016 International Computer Symposium (ICS)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Computer Symposium (ICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICS.2016.0064\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Computer Symposium (ICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICS.2016.0064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Abnormal Event Detection Using Microsoft Kinect in a Smart Home
In this paper, we present a continuous deep learning model for fall detection using Microsoft Kinect. The input include pre-processed high-resolution RGB images, depth images collected by a Kinect and optical flow images. We combine several deep learning structures including convolutional neural networks and long short-term memory networks for continuous human fallen detection. Finally, we present experimental results to demonstrate the performance and utility of our approach.