Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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An Efficient Vision-based Object Detection and Tracking using Online Learning

  • Kim, Byung-Gyu (Dept. of IT Engineering, Sookmyung Women's University) ;
  • Hong, Gwang-Soo (Dept. of Computer Engineering, SunMoon University) ;
  • Kim, Ji-Hae (Dept. of IT Engineering, Sookmyung Women's University) ;
  • Choi, Young-Ju (Dept. of IT Engineering, Sookmyung Women's University)
  • Received : 2017.12.06
  • Accepted : 2017.12.12
  • Published : 2017.12.31
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Abstract

In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.

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References

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