@inproceedings{1cdeb51693f6403191376dd5e59f9cda,
title = "Local sparse representation based interest point matching for person re-identification",
abstract = "This paper presents a multi-shot person re-identification system from video sequences based on Interest Points (SURFs) matching. Our objective is to improve the Interest Points (IPs) matching using low resolution images in terms of re-identification accuracy and running time. First, we propose a new method of SURF matching via Local Sparse Representation (LSR). Each SURF in the test video sequence is expressed as a sparse representation of a subset of SURFs in the reference dataset. Our approach consists of searching the latter subset from the reference IPs that are located on a similar spatial neighborhood to the query IP. Second, it investigates whether IPs filtering can decrease the re-identification running time. An ensemble of binary classifiers are evaluated. Our approach is assessed on the large dataset PRID-2011 and shown to outperform favorably with current state of the art.",
keywords = "Binary classifier, Filtering, Interest point, Local sparse representation, Person re-identification, SURF",
author = "Khedher, \{Mohamed Ibn\} and \{El Yacoubi\}, \{Mounim A.\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-26555-1\_28",
language = "English",
isbn = "9783319265544",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "241--250",
editor = "Tingwen Huang and Qingshan Liu and Lai, \{Weng Kin\} and Sabri Arik",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
}