Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Object Tracking based on Weight Sharing CNN Structure according to Search Area Setting Method Considering Object Movement

객체의 움직임을 고려한 탐색영역 설정에 따른 가중치를 공유하는 CNN구조 기반의 객체 추적

  • Kim, Jung Uk (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Ro, Yong Man (School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • Received : 2017.03.31
  • Accepted : 2017.05.29
  • Published : 2017.07.31
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Abstract

Object Tracking is a technique for tracking moving objects over time in a video image. Using object tracking technique, many research are conducted such a detecting dangerous situation and recognizing the movement of nearby objects in a smart car. However, it still remains a challenging task such as occlusion, deformation, background clutter, illumination variation, etc. In this paper, we propose a novel deep visual object tracking method that can be operated in robust to many challenging task. For the robust visual object tracking, we proposed a Convolutional Neural Network(CNN) which shares weight of the convolutional layers. Input of the CNN is a three; first frame object image, object image in a previous frame, and current search frame containing the object movement. Also we propose a method to consider the motion of the object when determining the current search area to search for the location of the object. Extensive experimental results on a authorized resource database showed that the proposed method outperformed than the conventional methods.

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References

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