Evaluation of Local Features for Person Re-Identification in Image Sequences

M. Bäuml, R. Stiefelhagen
International Conference on Advanced Video and Signal-based Surveillance (AVSS), Klagenfurt, August 2011
[paper] [bib]

Abstract

In this paper we present a comparative study of local features for the task of person re-identification. A combination of state-of-the-art interest point detectors and descriptors is evaluated. The experiments are performed on a novel dataset which we make publicly available for future research in this area. The results indicate that there are significant differences between the evaluated descriptors, with GLOH and SIFT outperforming both Shape Context and SURF descriptors. The evaluated interest point descriptors perform equally well, with a slight advantage for the Hessian-Laplace detector. The Harris-Affine and Hessian-Affine affine invariant region detectors do not provide any performance advantage and therefore do not justify their additional computational expense.

Dataset

lfpri_reid.tar.gz (1.6M) – CAVIAR Re-Identification dataset.