The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam makalah ini, kaedah pengesanan manusia dibangunkan. Pengesan berasaskan rupa dan pengesan berasaskan gerakan masing-masing dicadangkan. Histogram blok berbilang skala ciri templat (MB-HOT) digunakan untuk mengesan manusia melalui rupa. Ia menyepadukan maklumat nilai kelabu dan maklumat nilai kecerunan, dan mewakili perhubungan tiga blok. Percubaan pada set data INRIA menunjukkan bahawa ciri ini lebih diskriminasi berbanding ciri lain, seperti histogram kecerunan orientasi (HOG). Ciri berasaskan gerakan juga dicadangkan untuk menangkap gerakan relatif badan manusia. Ciri ini dikira dalam domain aliran optik dan hasil percubaan dalam set data kami menunjukkan bahawa ciri ini mengatasi ciri berasaskan gerakan lain. Respons pengesanan yang diperolehi oleh dua ciri digabungkan untuk mengurangkan pengesanan palsu. Pelaksanaan berasaskan unit proses grafik (GPU) dicadangkan untuk mempercepatkan pengiraan dua ciri, dan menjadikannya sesuai untuk aplikasi masa nyata.
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Salinan
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
Salinan
@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},}
Salinan
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 -