Abstract
Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem, person re-identification is expressed as a deep multi-instance learning issue. Therefore, a multiscale feature learning process is introduced which is driven by optimizing a novel cost function. We report on experiments and comparisons to other state-of-the-art algorithms using publicly available databases such as VIPeR and ETHZ.
| Original language | English |
|---|---|
| Title of host publication | 25th European Signal Processing Conference, EUSIPCO 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1559-1563 |
| Number of pages | 5 |
| Volume | 2017-January |
| ISBN (Electronic) | 9780992862671 |
| DOIs | |
| Publication status | Published - Oct 23 2017 |
| Event | 25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece Duration: Aug 28 2017 → Sep 2 2017 |
Other
| Other | 25th European Signal Processing Conference, EUSIPCO 2017 |
|---|---|
| Country | Greece |
| City | Kos |
| Period | 8/28/17 → 9/2/17 |
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ASJC Scopus subject areas
- Signal Processing
Cite this
Person re-identification based on deep multi-instance learning. / Varga, Domonkos; Szirányi, T.
25th European Signal Processing Conference, EUSIPCO 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1559-1563 8081471.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Person re-identification based on deep multi-instance learning
AU - Varga, Domonkos
AU - Szirányi, T.
PY - 2017/10/23
Y1 - 2017/10/23
N2 - Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem, person re-identification is expressed as a deep multi-instance learning issue. Therefore, a multiscale feature learning process is introduced which is driven by optimizing a novel cost function. We report on experiments and comparisons to other state-of-the-art algorithms using publicly available databases such as VIPeR and ETHZ.
AB - Person re-identification is one of the widely studied research topic in the fields of computer vision and pattern recognition. In this paper, we present a deep multi-instance learning approach for person re-identification. Since most publicly available databases for pedestrian re-identification are not enough big, over-fitting problems occur in deep learning architectures. To tackle this problem, person re-identification is expressed as a deep multi-instance learning issue. Therefore, a multiscale feature learning process is introduced which is driven by optimizing a novel cost function. We report on experiments and comparisons to other state-of-the-art algorithms using publicly available databases such as VIPeR and ETHZ.
UR - http://www.scopus.com/inward/record.url?scp=85041487640&partnerID=8YFLogxK
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U2 - 10.23919/EUSIPCO.2017.8081471
DO - 10.23919/EUSIPCO.2017.8081471
M3 - Conference contribution
AN - SCOPUS:85041487640
VL - 2017-January
SP - 1559
EP - 1563
BT - 25th European Signal Processing Conference, EUSIPCO 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -