한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제20권7호
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  • Pages.986-993
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  • 2017
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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객체의 움직임을 고려한 탐색영역 설정에 따른 가중치를 공유하는 CNN구조 기반의 객체 추적

Object Tracking based on Weight Sharing CNN Structure according to Search Area Setting Method Considering Object Movement

  • 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))
  • 투고 : 2017.03.31
  • 심사 : 2017.05.29
  • 발행 : 2017.07.31
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초록

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.

키워드

참고문헌

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