Multi-shot SURF-based person re-identification via sparse representation

Mohamed Ibn Khedher, Mounim A. El Yacoubi, Bernadette Dorizzi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present in this paper a multi-shot human reidentification system from video sequences based on SURF matching. Our contribution is about the matching step which is crucial. In this context, we propose a new method of SURF matching via sparse representation. Each SURF Interest Point in the test sequence is represented by a sparse representation of SURFs points in the reference dataset. For efficiency purposes, a dynamic dictionary is selected for each SURF from this dataset through KD-Tree Neighborhood search. Then a majority vote rule is applied to classify the test sequence. This approach is evaluated on two public datasets: PRID-2011 and CAVIAR4REID. The experimental results show that our approach compares favorably with and outperforms current state-of-the-art on the two datasets by 1% to 7%.

Original languageEnglish
Title of host publication2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
PublisherIEEE Computer Society
Pages159-164
Number of pages6
ISBN (Print)9781479907038
DOIs
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013 - Krakow, Poland
Duration: 27 Aug 201330 Aug 2013

Publication series

Name2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

Conference

Conference2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013
Country/TerritoryPoland
CityKrakow
Period27/08/1330/08/13

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