Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization

doi:10.1007/s11633-022-1321-8

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization
	Wei-Chen Chen, Xin-Yi Yu, Lin-Lin Ou
刊名	Machine Intelligence Research
	2022
卷号	19 页码:153-168
关键词	Pedestrian attribute recognition surveillance scenarios view-attribute attention mechanism localization
ISSN号	2731-538X
DOI	10.1007/s11633-022-1321-8
英文摘要	Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to the inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which utilizes view information to guide the recognition process to focus on specific attributes and attention mechanism to localize specific attribute-corresponding areas. Concretely, view information is leveraged by the view prediction branch to generate four view weights that represent the confidences for attributes from different views. View weights are then delivered back to compose specific view-attributes, which will participate and supervise deep feature extraction. In order to explore the spatial location of a view-attribute, regional attention is introduced to aggregate spatial information and encode inter-channel dependencies of the view feature. Subsequently, a fine attentive attribute-specific region is localized, and regional weights for the view-attribute from different spatial locations are gained by the regional attention. The final view-attribute recognition outcome is obtained by combining the view weights with the regional weights. Experiments on three wide datasets (richly annotated pedestrian (RAP), annotated pedestrian v2 (RAPv2), and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47402]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
推荐引用方式 GB/T 7714	Wei-Chen Chen, Xin-Yi Yu, Lin-Lin Ou. Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization[J]. Machine Intelligence Research,2022,19:153-168.
APA	Wei-Chen Chen, Xin-Yi Yu, Lin-Lin Ou.(2022).Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization.Machine Intelligence Research,19,153-168.
MLA	Wei-Chen Chen, Xin-Yi Yu, Lin-Lin Ou."Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization".Machine Intelligence Research 19(2022):153-168.