A Data Association Algorithm for People Re-Identification in Photo Sequences

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10 Citazioni (Scopus)

Abstract

In this paper, a new system is presented to support the user in the face annotation task. Every time a photo sequence becomes available, the system analyses it to detect and cluster faces in set corresponding to the same person. We propose to model the problem of people re-identification in photos as a data association problem. In this way, the system takes advantage from the assumption that each person can appear at most once in each photo. We propose a fully automated method for grouping facial images; the method does not require any initialization neither a priori knowledge of the number of persons that are in the photo sequence. We compare the results obtained with our method and with standard clustering methods on three personal collections and on a publicly available dataset.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2010

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Identification (control systems)

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

Cita questo

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title = "A Data Association Algorithm for People Re-Identification in Photo Sequences",
abstract = "In this paper, a new system is presented to support the user in the face annotation task. Every time a photo sequence becomes available, the system analyses it to detect and cluster faces in set corresponding to the same person. We propose to model the problem of people re-identification in photos as a data association problem. In this way, the system takes advantage from the assumption that each person can appear at most once in each photo. We propose a fully automated method for grouping facial images; the method does not require any initialization neither a priori knowledge of the number of persons that are in the photo sequence. We compare the results obtained with our method and with standard clustering methods on three personal collections and on a publicly available dataset.",
keywords = "Photo Album Management, Data Association, Re- Identification, Image databases",
author = "{La Cascia}, Marco and Marco Morana and {Lo Presti}, Liliana",
year = "2010",
language = "English",

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T1 - A Data Association Algorithm for People Re-Identification in Photo Sequences

AU - La Cascia, Marco

AU - Morana, Marco

AU - Lo Presti, Liliana

PY - 2010

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N2 - In this paper, a new system is presented to support the user in the face annotation task. Every time a photo sequence becomes available, the system analyses it to detect and cluster faces in set corresponding to the same person. We propose to model the problem of people re-identification in photos as a data association problem. In this way, the system takes advantage from the assumption that each person can appear at most once in each photo. We propose a fully automated method for grouping facial images; the method does not require any initialization neither a priori knowledge of the number of persons that are in the photo sequence. We compare the results obtained with our method and with standard clustering methods on three personal collections and on a publicly available dataset.

AB - In this paper, a new system is presented to support the user in the face annotation task. Every time a photo sequence becomes available, the system analyses it to detect and cluster faces in set corresponding to the same person. We propose to model the problem of people re-identification in photos as a data association problem. In this way, the system takes advantage from the assumption that each person can appear at most once in each photo. We propose a fully automated method for grouping facial images; the method does not require any initialization neither a priori knowledge of the number of persons that are in the photo sequence. We compare the results obtained with our method and with standard clustering methods on three personal collections and on a publicly available dataset.

KW - Photo Album Management, Data Association, Re- Identification, Image databases

UR - http://hdl.handle.net/10447/54097

M3 - Paper

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