Domain Adaptation for Pedestrian Detection Based on Prediction Consistency
Joint Authors
Li-ping, Yu
Huan-ling, Tang
Zhi-yong, An
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-10
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Pedestrian detection is an active area of research in computer vision.
It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene.
In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present.
Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain.
Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene.
American Psychological Association (APA)
Li-ping, Yu& Huan-ling, Tang& Zhi-yong, An. 2014. Domain Adaptation for Pedestrian Detection Based on Prediction Consistency. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1049053
Modern Language Association (MLA)
Li-ping, Yu…[et al.]. Domain Adaptation for Pedestrian Detection Based on Prediction Consistency. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1049053
American Medical Association (AMA)
Li-ping, Yu& Huan-ling, Tang& Zhi-yong, An. Domain Adaptation for Pedestrian Detection Based on Prediction Consistency. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1049053
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049053