IEICE TRANSACTIONS on Information
Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection
Summary :
Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.8 pp.1721-1724
- Publication Date
- 2011/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1721
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
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Chang LIU, Guijin WANG, Chunxiao LIU, Xinggang LIN, "Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 8, pp. 1721-1724, August 2011, doi: 10.1587/transinf.E94.D.1721.
Abstract: Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1721/_p
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@ARTICLE{e94-d_8_1721,
author={Chang LIU, Guijin WANG, Chunxiao LIU, Xinggang LIN, },
journal={IEICE TRANSACTIONS on Information},
title={Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection},
year={2011},
volume={E94-D},
number={8},
pages={1721-1724},
abstract={Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.},
keywords={},
doi={10.1587/transinf.E94.D.1721},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1721
EP - 1724
AU - Chang LIU
AU - Guijin WANG
AU - Chunxiao LIU
AU - Xinggang LIN
PY - 2011
DO - 10.1587/transinf.E94.D.1721
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2011
AB - Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.
ER -
English
