한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제19권12호
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  • Pages.1943-1950
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  • 2016
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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컨볼루션 신경망을 이용한 CCTV 영상 기반의 성별구분

CCTV Based Gender Classification Using a Convolutional Neural Networks

  • Kang, Hyun Gon (Dept. of Electronics Eng., Graduate School, Kyungsung University) ;
  • Park, Jang Sik (Dept. of Electronics Eng., Graduate School, Kyungsung University) ;
  • Song, Jong Kwan (Dept. of Electronics Eng., Graduate School, Kyungsung University) ;
  • Yoon, Byung Woo (Dept. of Electronics Eng., Graduate School, Kyungsung University)
  • 투고 : 2016.04.27
  • 심사 : 2016.11.29
  • 발행 : 2016.12.30
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초록

Recently, gender classification has attracted a great deal of attention in the field of video surveillance system. It can be useful in many applications such as detecting crimes for women and business intelligence. In this paper, we proposed a method which can detect pedestrians from CCTV video and classify the gender of the detected objects. So far, many algorithms have been proposed to classify people according the their gender. This paper presents a gender classification using convolutional neural network. The detection phase is performed by AdaBoost algorithm based on Haar-like features and LBP features. Classifier and detector is trained with data-sets generated form CCTV images. The experimental results of the proposed method is male matching rate of 89.9% and the results shows 90.7% of female videos. As results of simulations, it is shown that the proposed gender classification is better than conventional classification algorithm.

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참고문헌

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