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http://dx.doi.org/10.5392/JKCA.2016.16.09.451

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

Lim, Seung-Ouk (한밭대학교 정보통신전문대학원 멀티미디어공학과)
Kim, Yu-Seon (한밭대학교 정보통신전문대학원 멀티미디어공학과)
Lee, Si-Woong (한밭대학교 정보통신전문대학원 멀티미디어공학과)
Publication Information
The Journal of the Korea Contents Association / v.16, no.9, 2016 , pp. 451-459 More about this Journal
Abstract
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.
Keywords
Object Detection; Random Fern; RGB-D Image Format;
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