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Object Detection and Tracking in Real Time Videos

Author

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  • Christian R. Llano

    (Department of Industrial Engineering, University of Miami, Coral Gables, USA)

  • Yuan Ren

    (Department of Industrial Engineering, Shanghai Dianji University, Shanghai Shi, China)

  • Nazrul I. Shaikh

    (Department of Industrial Engineering, University of Miami, Coral Gables, USA)

Abstract

Object and human tracking in streaming videos are one of the most challenging problems in vision computing. In this article, we review some relevant machine learning algorithms and techniques for human identification and tracking in videos. We provide details on metrics and methods used in the computer vision literature for monitoring and propose a state-space representation of the object tracking problem. A proof of concept implementation of the state-space based object tracking using particle filters is presented as well. The proposed approach enables tracking objects/humans in a video, including foreground/background separation for object movement detection.

Suggested Citation

  • Christian R. Llano & Yuan Ren & Nazrul I. Shaikh, 2019. "Object Detection and Tracking in Real Time Videos," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global, vol. 11(2), pages 1-17, April.
    • Handle: RePEc:igg:jisss0:v:11:y:2019:i:2:p:1-17
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    Cited by:

    1. Gu, Wenbo & Ma, Tao & Li, Meng & Shen, Lu & Zhang, Yijie, 2020. "A coupled optical-electrical-thermal model of the bifacial photovoltaic module," Applied Energy, Elsevier, vol. 258(C).

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