skip to main content
10.1145/2536853.2536936acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmommConference Proceedingsconference-collections
research-article

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

Published: 02 December 2013 Publication History

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.

References

[1]
Kim, J. S., Kim, K. Y., Kim, H. I., and Kim, Y. S. 2012. A Video Annotation System with Automatic Human Detection from Video Surveillance Data. Journal of Korean Institute of Information Scientists and Engineers, Vol. 18, No. 11.
[2]
Witten, I. H., Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques 2nd Edition. Morgan Kaufmann.
[3]
Mackay, W. 1989. EVA: an Experimental Video Annotator for Symbolic Analysis of Video Data. SIGCHI Bulletin, Vol. 21.
[4]
Mariano, V. Y., Min, J., Park, J. H., Kasturi, R. et al. 2002. Performance evaluation of object detection algorithms. In Proceedings of the 16th International Conference on Pattern Recognition, Vol. 3. 965--969.
[5]
Zhu, X., Fan, J., Xue, X., Wu, L., and Elmagarmid, A. K. 2002. Semi-Automatic Video Content Annotation. LNCS 2532, Springer Verlag.
[6]
Khurana, L., Chandak, M. B. 2013. Study of Various Video Annotation Techniques. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 1.
[7]
Zaidenberg, S., Boulay, B., Bremond, F. 2012. A Generic Framework for Video Understanding Applied to Group Behavior Recognition. In Proceedings of IEEE Conference on Advanced Video and Signal-Based Surveillance.
[8]
Zhang, T., Xu, C., Zhu, G., Liu, S., and Lu, H. 2012. A Generic Framework for Video Annotation via Semi-Supervised Learning. IEEE Transactions on Multimedia, Vol. 14, No. 4.
[9]
Wazalwar, S. S., Malik, L. G. 2013. A Survey on Video Annotation Techniques. International Journal of Latest Trends in Engineering and Technology, Vol. 2, Issue 1.
[10]
Jeong, J. W., Hong, H. K., and Lee, D. H. 2011. Ontology-based Automatic Video Annotation Technique in Smart TV Environment. IEEE Transactions on Consumer Electronics, Vol. 57, No. 4.

Cited By

View all

Index Terms

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

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    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].

    In-Cooperation

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

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 02 December 2013

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Video surveillance
    2. data mining
    3. ground truth data
    4. intelligent object detection algorithm

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    MoMM '13

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 14 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Smart Annotation And Anonymizer2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724275(1-6)Online publication date: 24-Jun-2024
    • (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
    • (2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
    • (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
    • (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
    • (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

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media