无真值背景减法算法的评价

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance Pub Date : 2010-08-29 DOI:10.1109/AVSS.2010.21

Juan C. Sanmiguel, J. Sanchez

{"title":"无真值背景减法算法的评价","authors":"Juan C. Sanmiguel, J. Sanchez","doi":"10.1109/AVSS.2010.21","DOIUrl":null,"url":null,"abstract":"In video-surveillance systems, the moving objectsegmentation stage (commonly based on backgroundsubtraction) has to deal with several issues like noise,shadows and multimodal backgrounds. Hence, its failureis inevitable and its automatic evaluation is a desirablerequirement for online analysis. In this paper, we proposea hierarchy of existing performance measures not-basedon ground-truth for video object segmentation. Then, fourmeasures based on color and motion are selected andexamined in detail with different segmentation algorithmsand standard test sequences for video objectsegmentation. Experimental results show that color-basedmeasures perform better than motion-based measures andbackground multimodality heavily reduces the accuracy ofall obtained evaluation results","PeriodicalId":415758,"journal":{"name":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"On the Evaluation of Background Subtraction Algorithms without Ground-Truth\",\"authors\":\"Juan C. Sanmiguel, J. Sanchez\",\"doi\":\"10.1109/AVSS.2010.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In video-surveillance systems, the moving objectsegmentation stage (commonly based on backgroundsubtraction) has to deal with several issues like noise,shadows and multimodal backgrounds. Hence, its failureis inevitable and its automatic evaluation is a desirablerequirement for online analysis. In this paper, we proposea hierarchy of existing performance measures not-basedon ground-truth for video object segmentation. Then, fourmeasures based on color and motion are selected andexamined in detail with different segmentation algorithmsand standard test sequences for video objectsegmentation. Experimental results show that color-basedmeasures perform better than motion-based measures andbackground multimodality heavily reduces the accuracy ofall obtained evaluation results\",\"PeriodicalId\":415758,\"journal\":{\"name\":\"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AVSS.2010.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AVSS.2010.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 15

摘要

在视频监控系统中，运动目标分割阶段(通常基于背景减法)必须处理噪声、阴影和多模态背景等问题。因此，它的失败是不可避免的，它的自动评估是在线分析的理想要求。在本文中，我们提出了一种现有的性能度量的层次结构，用于视频对象分割。然后，选择了基于颜色和运动的四种度量，并使用不同的分割算法和标准的测试序列对视频目标分割进行了详细的研究。实验结果表明，基于颜色的度量比基于运动的度量性能更好，背景多模态严重降低了所有获得的评价结果的准确性

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

On the Evaluation of Background Subtraction Algorithms without Ground-Truth

In video-surveillance systems, the moving objectsegmentation stage (commonly based on backgroundsubtraction) has to deal with several issues like noise,shadows and multimodal backgrounds. Hence, its failureis inevitable and its automatic evaluation is a desirablerequirement for online analysis. In this paper, we proposea hierarchy of existing performance measures not-basedon ground-truth for video object segmentation. Then, fourmeasures based on color and motion are selected andexamined in detail with different segmentation algorithmsand standard test sequences for video objectsegmentation. Experimental results show that color-basedmeasures perform better than motion-based measures andbackground multimodality heavily reduces the accuracy ofall obtained evaluation results

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance

自引率

0.00%

发文量