Paper Info
Title
Person Re-Identification with Reference Descriptor
Abstract
Person identification across nonoverlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as wide-area surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches.
Year
DOI
Venue
2016
10.1109/TCSVT.2015.2416561
Circuits and Systems for Video Technology, IEEE Transactions  
Keywords
DocType
Volume
person re-identification,re-ranking,reference descriptor,saliency,subspace,surveillance
Journal
PP
Issue
ISSN
Citations 
99
1051-8215
38
PageRank 
References 
Authors
0.87
52
4
Name
Order
Citations
PageRank
Le An121711.24
Mehran Kafai2935.20
Songfan Yang334317.48
Bir Bhanu43356380.19