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http://dx.doi.org/10.5391/JKIIS.2016.26.4.259

Design of Upper Body Detection System Using RBFNN Based on HOG Algorithm  

Kim, Sun-Hwan (Department of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
Kim, Jin-Yul (Department of Electronic Engineering, The University of Suwon)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.26, no.4, 2016 , pp. 259-266 More about this Journal
Abstract
Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.
Keywords
Upper body detection; HOG; PCA; Pattern classifier; FCM-based RBFNNs;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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