15 1 2011 2 비디오 감시 응용을 위한 텍스쳐와 컬러 정보를 이용한 고속 물체 인식 이슬람 모하마드 카이룰 * , 자한 파라 * , 민재홍 * , 백중환 * Mohammad Khairul Islam * , Farah Jahan * , Jae-Hong Min * , and Joong-Hwan Baek * 요 약 . SURF . SURF . SURF . Bag of Word , Naïve Bayes . SIFT . 95.75% . Abstract In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Naïve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate. : SURF, SIFT, , , K- , Keywords: SURF, SIFT, Color Histogram, Bag of Words, K-Means, Naïve Bayes. * Dept. of Information & Telecommunication Engineering, Korea Aerospace University, Goyang-city, 412-791, Korea ‧ 1 (First Author) : (Mohammad Khairul Islam) ‧ : 2011 1 31 ‧ ( ) : 2011 1 31 ( : 2011 2 23 ) ‧ : 2011 2 28 I. Introduction Traditional video surveillance system equip with several closed-circuit televisions in important areas and a human operator for observing these monitors. However, the concurrent observation of several monitors and the long-term exhausting visualization cause problem of decaying human attention. To release a human being