MULTI-VIEW JOINT LEARNING NETWORK FOR PEDESTRIAN GENDER CLASSIFICATION
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) : Proceedings |
Publisher | IEEE |
Pages | 23-27 |
ISBN (Electronic) | 9781538621592 |
ISBN (Print) | 9781538621608 |
Publication status | Published - Nov 2017 |
Publication series
Name | International Symposium on Intelligent Signal Processing and Communication Systems ISPACS |
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Conference
Title | 25th IEEE International Symposium on Intelligent Signal Processing and Communication Systems 2017 (ISPACS 2017) |
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Location | Wanda Realm Xiamen North Bay Hotel |
Place | China |
City | Xiamen |
Period | 6 - 9 November 2017 |
Link(s)
Abstract
Since the object occlusion, view variation, arbitrary shape of pedestrian, etc., are inherited in the pedestrian images, the pedestrian gender classification is an extremely challenging task in the object recognition field. To address this problem, an effective multi-view joint learning network (MJLN) is proposed for pedestrian gender classification. Based on the observation that the view variation of pedestrian will affect the judgment of pedestrian gender, the proposed MJLN simultaneously performs pedestrian gender learning and pedestrian view learning. Consequently, the determination of pedestrian view (i.e., frontal, rear, and left/right profile) obtained by the pedestrian view learning module will effectively assist the pedestrian gender learning module to yield a more robust and distinguishable feature so as to improve the gender classification performance. Extensive experiments on multiple challenging pedestrian datasets, which includes CUHK, VIPeR, GRID, PRID, and MIT, have demonstrated that the proposed MJLN can effectively promote the gender classification performance, and outperforms multiple state-of-the-art methods.
Research Area(s)
- gender learning module, multiview joint learning network, Pedestrian gender classification, view learning module
Citation Format(s)
MULTI-VIEW JOINT LEARNING NETWORK FOR PEDESTRIAN GENDER CLASSIFICATION. / Cai, Lei; Zeng, Huanqiang; Zhu, Jianqing et al.
2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) : Proceedings. IEEE, 2017. p. 23-27 (International Symposium on Intelligent Signal Processing and Communication Systems ISPACS).Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review