{"title":"人类动作视频关键帧检测的深度学习方法","authors":"U. Gawande, K. Hajari, Yogesh Golhar","doi":"10.5772/intechopen.91188","DOIUrl":null,"url":null,"abstract":"A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of consti-tuting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.","PeriodicalId":183051,"journal":{"name":"Recent Trends in Computational Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Deep Learning Approach to Key Frame Detection in Human Action Videos\",\"authors\":\"U. Gawande, K. Hajari, Yogesh Golhar\",\"doi\":\"10.5772/intechopen.91188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of consti-tuting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.\",\"PeriodicalId\":183051,\"journal\":{\"name\":\"Recent Trends in Computational Intelligence\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Recent Trends in Computational Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5772/intechopen.91188\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Recent Trends in Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/intechopen.91188","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep Learning Approach to Key Frame Detection in Human Action Videos
A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of consti-tuting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.