In many surveillance tasks it is very important for security operators to know whether a specific person is present in a given scene, at a given position and time. Person rei-dentification deals with this problem in order to provide more efficient security. A novel distributed appearance-based method for person re-identification is proposed. Spatio-temporal features are used to group the camera network into camera neighbourhoods. A intra-neighbourhood camera confidence hand-over measure is computed by exploiting a signatures’ distance measure. The camera confidence measure is exploited to save network resources. Features that capture the chromatic appearance and the shape of an individual are used to compute a discriminative signature. The Expectation Maximization algorithm is used to fit Gaussian Mixture Models over the chromatic features. GMMs are exploited to compute the distance between signatures and to update the intra-neighbourhood camera confidence. The method has been validated using a benchmark dataset and a new dataset acquired from a wide camera network scenario.

Distributed Signature Fusion for Person Re-Identification

MARTINEL, Niki;MICHELONI, Christian;PICIARELLI, Claudio
2012-01-01

Abstract

In many surveillance tasks it is very important for security operators to know whether a specific person is present in a given scene, at a given position and time. Person rei-dentification deals with this problem in order to provide more efficient security. A novel distributed appearance-based method for person re-identification is proposed. Spatio-temporal features are used to group the camera network into camera neighbourhoods. A intra-neighbourhood camera confidence hand-over measure is computed by exploiting a signatures’ distance measure. The camera confidence measure is exploited to save network resources. Features that capture the chromatic appearance and the shape of an individual are used to compute a discriminative signature. The Expectation Maximization algorithm is used to fit Gaussian Mixture Models over the chromatic features. GMMs are exploited to compute the distance between signatures and to update the intra-neighbourhood camera confidence. The method has been validated using a benchmark dataset and a new dataset acquired from a wide camera network scenario.
2012
9781450317726
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/883257
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 30
  • ???jsp.display-item.citation.isi??? 0
social impact