Skip to Main content Skip to Navigation
Conference papers

Person re-identification by pose priors

Abstract : The person re-identification problem is a well known retrieval task that requires finding a person of interest in a network of cameras. In a real-world scenario, state of the art algorithms are likely to fail due to serious perspective and pose changes as well as variations in lighting conditions across the camera network. The most effective approaches try to cope with all these changes by applying metric learning tools to find a transfer function between a camera pair. Unfortunately, this transfer function is usually dependent on the camera pair and requires labeled training data for each camera. This might be unattainable in a large camera network. In this paper, instead of learning the transfer function that addresses all appearance changes, we propose to learn a generic metric pool that only focuses on pose changes. This pool consists of metrics, each one learned to match a specific pair of poses. Automatically estimated poses determine the proper metric, thus improving matching. We show that metrics learned using a single camera improve the matching across the whole camera network, providing a scalable solution. We validated our approach on a publicly available dataset demonstrating increase in the re-identification performance.
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download

https://hal.inria.fr/hal-01849277
Contributor : Soumik Mallick <>
Submitted on : Wednesday, July 25, 2018 - 6:03:31 PM
Last modification on : Monday, August 5, 2019 - 4:20:05 PM
Long-term archiving on: : Friday, October 26, 2018 - 4:11:59 PM

File

bak-spie2015.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01849277, version 1

Collections

Citation

Slawomir Bak, Filipe Martins, François Bremond. Person re-identification by pose priors. IS&T/SPIE Electronic Imaging, Feb 2015, San Francisco, United States. ⟨hal-01849277⟩

Share

Metrics

Record views

144

Files downloads

161