• Title/Summary/Keyword: 곡률-HOG

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Pedestrian Recognition using Adaboost Algorithm based on Cascade Method by Curvature and HOG (곡률과 HOG에 의한 연속 방법에 기반한 아다부스트 알고리즘을 이용한 보행자 인식)

  • Lee, Yeung-Hak;Ko, Joo-Young;Suk, Jung-Hee;Roh, Tae-Moon;Shim, Jae-Chang
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.6
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    • pp.654-662
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    • 2010
  • In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using second-stage cascade method, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: (i) Histogram of Oriented Gradient (HOG) which includes gradient information and differential magnitude; (ii) Curvature-HOG which is based on four different curvature features per pixel. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using both HOG and curvature-HOG. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. For the recognition-failed image, the other feature and strong classification will be used for the second stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method.