Sparse representations based distributed attribute learning for person re-identification

Sparse representations based distributed attribute learning for person re-identification Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, and from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Sparse Representations based Distributed Attribute Learning Model (SRDAL) to encode targets into semantic topics. Compared to other models such as ELF, our model performs best by imposing semantic restrictions onto the generation of human specific attributes and employing Deep Convolutional Neural Network to generate features without supervision for attributes learning model. Experimental results show that our method achieves state-of-the-art performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Sparse representations based distributed attribute learning for person re-identification

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4967-4
Publisher site
See Article on Publisher Site

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