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Deep Multi-View Feature Learning for Person Re-Identification.
- Source :
- IEEE Transactions on Circuits & Systems for Video Technology; Oct2018, Vol. 28 Issue 10, p2657-2666, 10p
- Publication Year :
- 2018
-
Abstract
- Person re-identification aims to identify the same pedestrians across different camera views at different locations. This important yet difficult intelligent video analysis problem remains a vigorous area of research due to demands for performance improvements. Person re-identification involves two main steps: feature representation and metric learning. Handcrafted features, such as color and texture histograms, are frequently used for person re-identification, but most handcrafted features are limited by not being directly applicable to practical problems. Deep learning methods have obtained the state-of-the-art performance in a wide variety of applications, including image annotation, face recognition, and speech recognition. However, deep learning features are heavily dependent on large-scale labeling of samples. In this paper, by utilizing the Cross-view Quadratic Discriminant Analysis (XQDA) metric learning, we propose a novel scheme called deep multi-view feature learning (DMVFL), which exploits the collaboration between handcrafted and deep learning features in a simple but effective way. Furthermore, we prove that the XQDA is a robust algorithm. Extensive experiments on two challenging person re-identification data sets (VIPeR and GRID) demonstrate that DMVFL improves on current state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 28
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 132683757
- Full Text :
- https://doi.org/10.1109/TCSVT.2017.2726580