@inproceedings{b4494b2b639b46208f09fc2d167ce6cf,
title = "Deep similarity feature learning for person re-identification",
abstract = "Person re-identification aims to match the same pedestrians across different camera views and has been applied to many important applications such as intelligent video surveillance. Due to the spatiotemporal uncertainty and visual ambiguity of pedestrian image pairs, person re-identification remains a difficult and challenging problem. The huge success of deep learning has focused attention on the use of deep features for person re-identification. However, for person re-identification, most deep learning methods minimize cross-entropy or triplet-based losses, thereby neglecting the fact that the similarities and differences between image pairs can be considered simultaneously to increase discrimination. In this paper, we propose a novel deep learning method called deep similarity feature learning (DSFL) to extract more effective deep features for image pairs. Extensive experiments on two representative person re-identification datasets (CUHK-03 and GRID) demonstrate the effectiveness and robustness of DSFL.",
keywords = "Deep features, Deep learning, Person re-identification, Video surveillance",
author = "Yanan Guo and Dapeng Tao and Jun Yu and Yaotang Li",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th Pacific-Rim Conference on Multimedia, PCM 2016 ; Conference date: 15-09-2016 Through 16-09-2016",
year = "2016",
doi = "10.1007/978-3-319-48890-5\_38",
language = "英语",
isbn = "9783319488899",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "386--396",
editor = "Enqing Chen and Yun Tie and Yihong Gong",
booktitle = "Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings",
address = "德国",
}