Chen Hongkai; Zhao Xiaoguang; Sun Shiying; Tan Min
Source Publicationinternationaljournalofautomationandcomputing
AbstractIn this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis (PLS-CCA) for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve (AUC) value at the same time, and offers a discriminative and stable recognition performance.
Document Type期刊论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen Hongkai,Zhao Xiaoguang,Sun Shiying,et al. plsccaheterogeneousfeaturesfusionbasedlowresolutionhumandetectionmethodforoutdoorvideosurveillance[J]. internationaljournalofautomationandcomputing,2017,14(2):136.
APA Chen Hongkai,Zhao Xiaoguang,Sun Shiying,&Tan Min.(2017).plsccaheterogeneousfeaturesfusionbasedlowresolutionhumandetectionmethodforoutdoorvideosurveillance.internationaljournalofautomationandcomputing,14(2),136.
MLA Chen Hongkai,et al."plsccaheterogeneousfeaturesfusionbasedlowresolutionhumandetectionmethodforoutdoorvideosurveillance".internationaljournalofautomationandcomputing 14.2(2017):136.
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