In this paper we present a comparative study of local features for the task of person (re-)identification. A com bination of state-of-the-art interest point detectors and de scriptors is evaluated. The experiments are peiformed on a novel dataset which we make publicly available for future research in this area. The results indicate that there are sig nificant diff erences between the evaluated descriptors, with GLOH and SIFT outpeiforming both Shape Context and SURF descriptors. The evaluated interest point descrip tors peiform 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 peiformance advantage and therefore do not justify their additional computational expense.
Bauml and Stiefelhagen REF evaluated different local features for person re-identification in image sequences.
Evaluation of local features for person re-identification in image sequences
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Computer Science