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A Semi-Automatic Video Annotation Tool to Generate Ground Truth for Intelligent Video Surveillance Systems

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Yoo-Sung KimAuthors Info & Claims

MoMM '13: Proceedings of International Conference on Advances in Mobile Computing & Multimedia

Pages 509 - 513

https://doi.org/10.1145/2536853.2536936

Published: 02 December 2013 Publication History

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Abstract

Since generating ground truth data for developing object detection algorithms of intelligent surveillance systems is very important, a user-friendly tool to annotate videos efficiently and accurately is essential. In this paper, a semi-automatic video annotation tool is developed. For efficiency, the developed tool can automatically generate the initial annotation data for input videos by the automatic object detection modules which are developed independently and registered. To guarantee the accuracy of the ground truth data, the system also has several user-friendly functions to support the users to check the validity of the initial annotation data. With the developed video annotation tool, users can generate large amount of ground truth data for many videos.

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Cited By

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Kim HKim BLu WLi L(2023)No-code MLOps Platform for Data Annotation2023 IEEE International Conference on Memristive Computing and Applications (ICMCA)10.1109/ICMCA59770.2023.10480992(1-6)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICMCA59770.2023.10480992
Wilchek MHanley WLim JLuther KBatarseh F(2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106376
Zare SYazdi M(2022)A Survey on Semi-Automated and Automated Approaches for Video Annotation2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE57176.2022.9960039(404-409)Online publication date: 17-Nov-2022
https://doi.org/10.1109/ICCKE57176.2022.9960039
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A Semi-Automatic Video Annotation Tool to Generate Ground Truth for Intelligent Video Surveillance Systems
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

MoMM '13: Proceedings of International Conference on Advances in Mobile Computing & Multimedia

December 2013

599 pages

ISBN:9781450321068

DOI:10.1145/2536853

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 December 2013

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MoMM '13

MoMM '13: The 11th International Conference on Advances in Mobile Computing & Multimedia

December 2 - 4, 2013

Vienna, Austria

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Kim HKim BLu WLi L(2023)No-code MLOps Platform for Data Annotation2023 IEEE International Conference on Memristive Computing and Applications (ICMCA)10.1109/ICMCA59770.2023.10480992(1-6)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICMCA59770.2023.10480992
Wilchek MHanley WLim JLuther KBatarseh F(2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106376
Zare SYazdi M(2022)A Survey on Semi-Automated and Automated Approaches for Video Annotation2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE57176.2022.9960039(404-409)Online publication date: 17-Nov-2022
https://doi.org/10.1109/ICCKE57176.2022.9960039
Sager CJaniesch CZschech P(2021)A survey of image labelling for computer vision applicationsJournal of Business Analytics10.1080/2573234X.2021.19088614:2(91-110)Online publication date: 18-Apr-2021
https://doi.org/10.1080/2573234X.2021.1908861
Ahn HKim DKim YChoi JHan SLee JPark TLeung CKim YLee Y(2015)Generating New Ground Truth Data by Editing Previous Data from Integrated Video Annotation DatabaseProceedings of the 2015 International Conference on Big Data Applications and Services10.1145/2837060.2837097(208-212)Online publication date: 20-Oct-2015
https://dl.acm.org/doi/10.1145/2837060.2837097

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