{"title":"Video shot classification using 2D motion histogram","authors":"Pawin Prasertsakul, T. Kondo, Hiroyuki Iida","doi":"10.1109/ECTICON.2017.8096208","DOIUrl":null,"url":null,"abstract":"This paper describes a new histogram based approach for classifying camera motions or characterizing video shots. The proposed method utilizes both magnitudes and orientations of motion vectors simultaneously, rather than using them separately as same as the existing methods. A 2D histogram, namely 2D array motion vector histogram, that carries both magnitudes and orientations of motion vectors detected in video sequences. We have compared the proposed approach with existing methods. Experimental results show that the proposed method can classify more camera motions with better performance.","PeriodicalId":273911,"journal":{"name":"2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECTICON.2017.8096208","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper describes a new histogram based approach for classifying camera motions or characterizing video shots. The proposed method utilizes both magnitudes and orientations of motion vectors simultaneously, rather than using them separately as same as the existing methods. A 2D histogram, namely 2D array motion vector histogram, that carries both magnitudes and orientations of motion vectors detected in video sequences. We have compared the proposed approach with existing methods. Experimental results show that the proposed method can classify more camera motions with better performance.