Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification

Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, Yonghong Tian; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1306-1315

Abstract


Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in real-world applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task dictionary learning method which is able to learn a dataset-shared but target-data-biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state-of-the-art.

Related Material


[pdf]
[bibtex]
@InProceedings{Peng_2016_CVPR,
author = {Peng, Peixi and Xiang, Tao and Wang, Yaowei and Pontil, Massimiliano and Gong, Shaogang and Huang, Tiejun and Tian, Yonghong},
title = {Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}