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Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification.

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成果类型:
期刊论文
作者:
Gao, Changxin;Wang, Jin;Liu, Leyuan;Yu, Jin-Gang;Sang, Nong*
通讯作者:
Sang, Nong
作者机构:
[Wang, Jin; Gao, Changxin; Sang, Nong] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Minist Educ Image Proc & Intelligent Control, Key Lab, Wuhan 430074, Hubei, Peoples R China.
[Liu, Leyuan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
[Yu, Jin-Gang] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China.
通讯机构:
[Sang, Nong] H
Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Minist Educ Image Proc & Intelligent Control, Key Lab, Wuhan 430074, Hubei, Peoples R China.
语种:
英文
关键词:
person re-identification;superpixel;temporally aligned pooling;walking cycle
期刊:
Sensors
ISSN:
1424-3210
年:
2019
卷:
19
期:
18
页码:
3861
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61876210]; Natural Science Foundation of Hubei ProvinceNatural Science Foundation of Hubei Province [2018CFB426]
机构署名:
本校为其他机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space–time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we prop...

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