{"title":"用于冻结伪影检测的慢动作视频序列数据库","authors":"Ela Vrtar, M. Herceg, M. Vranješ, Danijel Babic","doi":"10.1109/ZINC58345.2023.10174092","DOIUrl":null,"url":null,"abstract":"In this paper, a new video sequence database, called Slow Motion Video Sequences (SMVS), is developed. The developed SMVS database consists of 30 video sequences with very low temporal activities, where every sequence contains a freezing artifact. The performance of two freezing detection algorithms, the Histogram-Based Freezing Artifacts Detection Algorithm (HBFDA) and the Real-Time no-reference Freezing Detection Algorithm (RTFDA) are tested on the developed database. The testing results show the poor performance of the tested algorithms.","PeriodicalId":383771,"journal":{"name":"2023 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Slow motion video sequences database for freezing artifact detection\",\"authors\":\"Ela Vrtar, M. Herceg, M. Vranješ, Danijel Babic\",\"doi\":\"10.1109/ZINC58345.2023.10174092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new video sequence database, called Slow Motion Video Sequences (SMVS), is developed. The developed SMVS database consists of 30 video sequences with very low temporal activities, where every sequence contains a freezing artifact. The performance of two freezing detection algorithms, the Histogram-Based Freezing Artifacts Detection Algorithm (HBFDA) and the Real-Time no-reference Freezing Detection Algorithm (RTFDA) are tested on the developed database. The testing results show the poor performance of the tested algorithms.\",\"PeriodicalId\":383771,\"journal\":{\"name\":\"2023 Zooming Innovation in Consumer Technologies Conference (ZINC)\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 Zooming Innovation in Consumer Technologies Conference (ZINC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ZINC58345.2023.10174092\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Zooming Innovation in Consumer Technologies Conference (ZINC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ZINC58345.2023.10174092","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Slow motion video sequences database for freezing artifact detection
In this paper, a new video sequence database, called Slow Motion Video Sequences (SMVS), is developed. The developed SMVS database consists of 30 video sequences with very low temporal activities, where every sequence contains a freezing artifact. The performance of two freezing detection algorithms, the Histogram-Based Freezing Artifacts Detection Algorithm (HBFDA) and the Real-Time no-reference Freezing Detection Algorithm (RTFDA) are tested on the developed database. The testing results show the poor performance of the tested algorithms.
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