{"title":"基于三维动作识别的视频监控系统","authors":"Sungjoo Park, Dongchil Kim","doi":"10.1109/ICUFN.2018.8436644","DOIUrl":null,"url":null,"abstract":"Human action recognition using depth-map images from 3D camera for surveillance system is a promising alternative to the conventional 2D video based surveillance. We propose a security-event detection method based on body part classification and human action recognition for more effective video surveillance system. Experimental results show that the body part classification accuracy of 65.0% and security event detection accuracy of 0.878 were achieved for 9 security events.","PeriodicalId":224367,"journal":{"name":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"48 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Video Surveillance System Based on 3D Action Recognition\",\"authors\":\"Sungjoo Park, Dongchil Kim\",\"doi\":\"10.1109/ICUFN.2018.8436644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Human action recognition using depth-map images from 3D camera for surveillance system is a promising alternative to the conventional 2D video based surveillance. We propose a security-event detection method based on body part classification and human action recognition for more effective video surveillance system. Experimental results show that the body part classification accuracy of 65.0% and security event detection accuracy of 0.878 were achieved for 9 security events.\",\"PeriodicalId\":224367,\"journal\":{\"name\":\"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)\",\"volume\":\"48 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICUFN.2018.8436644\",\"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 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICUFN.2018.8436644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Video Surveillance System Based on 3D Action Recognition
Human action recognition using depth-map images from 3D camera for surveillance system is a promising alternative to the conventional 2D video based surveillance. We propose a security-event detection method based on body part classification and human action recognition for more effective video surveillance system. Experimental results show that the body part classification accuracy of 65.0% and security event detection accuracy of 0.878 were achieved for 9 security events.
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