@inproceedings{7e1ef17afe63437b94c9ad56202695fa,
title = "Asymmetric cross-view dictionary learning for person re-identification",
abstract = "Person re-identification is a critical yet challenging task in video surveillance which intends to match people over non-overlapping cameras. Most metric learning algorithms for person re-identification use symmetric matrix to project feature vectors into the same subspace to compute the similarity while ignoring the discrepancy between views. To solve this problem, we proposed an asymmetric cross-view matching algorithm with dictionary learning to alleviate the variations in human appearance across different views. Not only the views' dictionaries but also the persons' dictionary codes are constrained. Moreover, the 'between-class' and the 'within-class' distance are taken into consideration which makes the forming dictionary codes more robust and discriminative than the original feature vectors. The effectiveness of our approach is validated on the VIPeR and CUHK01 datasets. Experimental results show the proposed algorithm achieves compelling performance and asymmetric model plays an important role in the proposed approach.",
keywords = "cross-view matching, dictionary learning, Person re-identification",
author = "Minyue Jiang and Yuan Yuan and Qi Wang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952352",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1228--1232",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}