IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v8y2014i1p51-66.html
   My bibliography  Save this article

Generating Semantic Annotation of Video for Organizing and Searching Traffic Resources

Author

Listed:
  • Zheng Xu

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Fenglin Zhi

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Chen Liang

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Lin Mei

    (The Third Research Institute of Ministry of Public Security, Shanghai, China)

  • Xiangfeng Luo

    (Shanghai University, Shanghai, China)

Abstract

Image and video resources play an important role in traffic events analysis. With the rapid growth of the video surveillance devices, large number of image and video resources is increasing being created. It is crucial to explore, share, reuse, and link these multimedia resources for better organizing traffic events. Most of the video resources are currently annotated in an isolated way, which means that they lack semantic connections. Thus, providing the facilities for annotating these video resources is highly demanded. These facilities create the semantic connections among video resources and allow their metadata to be understood globally. Adopting semantic technologies, this paper introduces a video annotation platform. The platform enables user to semantically annotate video resources using vocabularies defined by traffic events ontologies. Moreover, the platform provides the search interface of annotated video resources. The result of initial development demonstrates the benefits of applying semantic technologies in the aspects of reusability, scalability and extensibility.

Suggested Citation

  • Zheng Xu & Fenglin Zhi & Chen Liang & Lin Mei & Xiangfeng Luo, 2014. "Generating Semantic Annotation of Video for Organizing and Searching Traffic Resources," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 8(1), pages 51-66, January.
    • Handle: RePEc:igg:jcini0:v:8:y:2014:i:1:p:51-66
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2014010104
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liping Yang & Bin Yang & Xiaohua Gu, 2021. "Adversarial Reconstruction CNN for Illumination-Robust Frontal Face Image Recovery and Recognition," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 18-33, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jcini0:v:8:y:2014:i:1:p:51-66. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.