VISUAL CATEGORIZATION METHOD WITH A BAG OF PCA PACKED KEYPOINTS

摘要：

Visual categorization is one of a key function in the next generation of a driving assist system, which is expected to re-duce a traffic accident. This paper proposes a high performance visual categorization method, which is based on Feature Acce-lerated Segment Test (FAST) feature point detectors, Histograms of Oriented Gradients (HOG) feature descriptors and Bag-of-Keypoints (BoK). Each feature descriptors were ortho-gonalized by applying the Principal Component Analysis (PCA) to reduce the size of dimension. As a result, our pro-posed method has achieved the recognition rate of 69.5％ and the performance of 43.1 ms on a PC in order to categorize one object in an image into traffic related categories, i.e. pedestrians, cars, bikes, bicycles, and so on. The comparison with conven-tional methods will be also discussed.

关键词： Visual categorization FAST HOG PCA

作者: Sho Okumura Naoya Maeda Kiyoshi Nakata Kazunori Saito Yohei Fukumizu Hironori Yamauchi

作者单位: Graduate School of Science and Engineering Ritsumeikan University 1-1-1, Nojihigashi, Kusatsu, Shiga Renesas Electronics Corporation SoC Software Platform Division SoC Software Platform Department 3 4-

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 964-967

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）