IEICE TRANSACTIONS on Information
Accurate Human Detection by Appearance and Motion
Summary :
In this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.10 pp.2728-2736
- Publication Date
- 2010/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.2728
- Type of Manuscript
- Special Section PAPER (Special Section on Data Mining and Statistical Science)
- Category
Authors
Keyword
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Shaopeng TANG, Satoshi GOTO, "Accurate Human Detection by Appearance and Motion" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2728-2736, October 2010, doi: 10.1587/transinf.E93.D.2728.
Abstract: In this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2728/_p
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@ARTICLE{e93-d_10_2728,
author={Shaopeng TANG, Satoshi GOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Accurate Human Detection by Appearance and Motion},
year={2010},
volume={E93-D},
number={10},
pages={2728-2736},
abstract={In this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications.},
keywords={},
doi={10.1587/transinf.E93.D.2728},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Accurate Human Detection by Appearance and Motion
T2 - IEICE TRANSACTIONS on Information
SP - 2728
EP - 2736
AU - Shaopeng TANG
AU - Satoshi GOTO
PY - 2010
DO - 10.1587/transinf.E93.D.2728
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - In this paper, a human detection method is developed. An appearance based detector and a motion based detector are proposed respectively. A multi scale block histogram of template feature (MB-HOT) is used to detect human by the appearance. It integrates the gray value information and the gradient value information, and represents the relationship of three blocks. Experiment on INRIA dataset shows that this feature is more discriminative than other features, such as histogram of orientation gradient (HOG). A motion based feature is also proposed to capture the relative motion of human body. This feature is calculated in optical flow domain and experimental result in our dataset shows that this feature outperforms other motion based features. The detection responses obtained by two features are combined to reduce the false detection. Graphic process unit (GPU) based implementation is proposed to accelerate the calculation of two features, and make it suitable for real time applications.
ER -
English
