Visual categorization method with a Bag of PCA packed Keypoints
Visual categorization is one of a key function in the next generation of a driving assist system, which is expected to reduce a traffic accident. This paper proposes a high performance visual categorization method, which is based on Feature Accelerated Segment Test (FAST) feature point detectors, Hi...
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| creator | Okumura, S. Maeda, N. Nakata, K. Saito, K. Fukumizu, Y. Yamauchi, H. |
| description | Visual categorization is one of a key function in the next generation of a driving assist system, which is expected to reduce a traffic accident. This paper proposes a high performance visual categorization method, which is based on Feature Accelerated Segment Test (FAST) feature point detectors, Histograms of Oriented Gradients (HOG) feature descriptors and Bag-of-Keypoints (BoK). Each feature descriptors were orthogonalized by applying the Principal Component Analysis (PCA) to reduce the size of dimension. As a result, our proposed 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 conventional methods will be also discussed. |
| doi_str_mv | 10.1109/CISP.2011.6100330 |
| format | Conference Proceeding |
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| ispartof | 2011 4th International Congress on Image and Signal Processing, 2011, Vol.2, p.950-953 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Brightness Detectors FAST Feature extraction Histograms HOG PCA Principal component analysis Vectors Visual categorization Visualization |
| title | Visual categorization method with a Bag of PCA packed Keypoints |
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