Object Detection Using Combined Random Fern for RGB-D Image Format

RGB-D 영상 포맷을 위한 결합형 무작위 Fern을 이용한 객체 검출

  • 임승욱 (한밭대학교 정보통신전문대학원 멀티미디어공학과) ;
  • 김유선 (한밭대학교 정보통신전문대학원 멀티미디어공학과) ;
  • 이시웅 (한밭대학교 정보통신전문대학원 멀티미디어공학과)
  • Received : 2016.06.02
  • Accepted : 2016.08.02
  • Published : 2016.09.28


While an object detection algorithm plays a key role in many computer vision applications, it requires extensive computation to show robustness under varying lightning and geometrical distortions. Recently, some approaches formulate the problem in a classification framework and show improved performances in object recognition. Among them, random fern algorithm drew a lot of attention because of its simple structure and high recognition rates. However, it reveals performance degradation under the illumination changes and noise addition, since it computes patch features based only on pixel intensities. In this paper, we propose a new structure of combined random fern which incorporates depth information into the conventional random fern reflecting 3D structure of the patch. In addition, a new structure of object tracker which exploits the combined random fern is also introduced. Experiments show that the proposed method provides superior performance of object detection under illumination change and noisy condition compared to the conventional methods.


Supported by : 한밭대학교


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