%0 Journal Article
%T Person Re-identification by Iterative Re-weighted Sparse Ranking
%A G. Lisanti
%A I. Masi
%A Andrew Bagdanov
%A Alberto del Bimbo
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%D 2015
%V 37
%N 8
%@ 0162-8828
%F G. Lisanti2015
%O LAMP; 601.240; 600.079
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2557), last updated on Tue, 20 Oct 2015 13:08:17 +0200
%X In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.
%U http://dx.doi.org/10.1109/TPAMI.2014.2369055
%P 1629-1642