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Semi-supervised Elastic net for pedestrian counting
Tan, Ben1; Zhang, Junping1; Wang, Liang2
AbstractPedestrian counting plays an important role in public safety and intelligent transportation. Most pedestrian counting algorithms based on supervised learning require much labeling work and rarely exploit the topological information of unlabelled data in a video. In this paper, we propose a Semi-Supervised Elastic Net (SSEN) regression method by utilizing sequential information between unlabelled samples and their temporally neighboring samples as a regularization term. Compared with a state-of-the-art algorithm, extensive experiments indicate that our algorithm can not only select sparse representative features from the original feature space without losing their interpretability, but also attain superior prediction performance with only very few labelled frames. (C) 2010 Elsevier Ltd. All rights reserved.
KeywordSemi-supervised Regression Elastic Net Pedestrian Counting Feature Selection Statistical Landscape Features
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000292849000008
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Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
Recommended Citation
GB/T 7714
Tan, Ben,Zhang, Junping,Wang, Liang. Semi-supervised Elastic net for pedestrian counting[J]. PATTERN RECOGNITION,2011,44(10-11):2297-2304.
APA Tan, Ben,Zhang, Junping,&Wang, Liang.(2011).Semi-supervised Elastic net for pedestrian counting.PATTERN RECOGNITION,44(10-11),2297-2304.
MLA Tan, Ben,et al."Semi-supervised Elastic net for pedestrian counting".PATTERN RECOGNITION 44.10-11(2011):2297-2304.
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