{"title":"从视频中识别印度舞蹈形式","authors":"A. Bisht, Riya Bora, Goutam Saini, Pushkar Shukla","doi":"10.1109/SITIS.2017.30","DOIUrl":null,"url":null,"abstract":"Classical dance forms are an integral part of the Indian culture and heritage. Therefore, preserving and comprehending these dance forms is a relevant problem in context with the digital preservation of the Indian Heritage. In this paper, we propose a novel framework to classify Indian classical dance forms from videos. The representations are then extracted through Deep Convolution Neural Network(DCNN) and Optical Flow. Moreover these representations are trained on a multi-class linear support vector machine(SVM). Furthermore, a novel dataset is introduced to evaluate the performance of the proposed framework. The framework is able to achieve the accuracy of 75.83 % when tested on 211 videos.","PeriodicalId":153165,"journal":{"name":"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Indian Dance Form Recognition from Videos\",\"authors\":\"A. Bisht, Riya Bora, Goutam Saini, Pushkar Shukla\",\"doi\":\"10.1109/SITIS.2017.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Classical dance forms are an integral part of the Indian culture and heritage. Therefore, preserving and comprehending these dance forms is a relevant problem in context with the digital preservation of the Indian Heritage. In this paper, we propose a novel framework to classify Indian classical dance forms from videos. The representations are then extracted through Deep Convolution Neural Network(DCNN) and Optical Flow. Moreover these representations are trained on a multi-class linear support vector machine(SVM). Furthermore, a novel dataset is introduced to evaluate the performance of the proposed framework. The framework is able to achieve the accuracy of 75.83 % when tested on 211 videos.\",\"PeriodicalId\":153165,\"journal\":{\"name\":\"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)\",\"volume\":\"412 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SITIS.2017.30\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SITIS.2017.30","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classical dance forms are an integral part of the Indian culture and heritage. Therefore, preserving and comprehending these dance forms is a relevant problem in context with the digital preservation of the Indian Heritage. In this paper, we propose a novel framework to classify Indian classical dance forms from videos. The representations are then extracted through Deep Convolution Neural Network(DCNN) and Optical Flow. Moreover these representations are trained on a multi-class linear support vector machine(SVM). Furthermore, a novel dataset is introduced to evaluate the performance of the proposed framework. The framework is able to achieve the accuracy of 75.83 % when tested on 211 videos.