객체의 움직임을 고려한 탐색영역 설정에 따른 가중치를공유하는 CNN구조 기반의 객체 추적 Object Tracking based on Weight Sharing CNN Structure according to Search Area Setting Method Considering Object Movement

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.
Publisher
한국멀티미디어학회
Issue Date
2017-07
Language
Korean
Citation

멀티미디어학회논문지, v.20, no.7, pp.986 - 993

ISSN
1229-7771
URI
http://hdl.handle.net/10203/225673
Appears in Collection
EE-Journal Papers(저널논문)
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