Signature-based matching has been the dominant choice for state-of-the-art person re-identification across multiple disjoint cameras. An approach that exploits image dissimilarities is proposed, treating re-identification as a binary classification problem. To achieve the objective, the person re-identification problem is addressed as follows: (i) first, compute the image dissimilarity between a pair of images acquired from two disjoint cameras; (ii) then learn the linear subspace where the image dissimilarities lie in an unsupervised fashion and (iii) lastly train a binary classifier in the linear subspace to discriminate between image dissimilarities computed for a positive pair (images are for the same person) and a negative pair (images are for different persons). An approach on two publicly available benchmark datasets is evaluated and compared with state-of-the-art methods for person re-identification. © The Institution of Engineering and Technology 2014.

Person re-identification by modelling principal component analysis coefficients of image dissimilarities

MARTINEL, Niki;MICHELONI, Christian
2014-01-01

Abstract

Signature-based matching has been the dominant choice for state-of-the-art person re-identification across multiple disjoint cameras. An approach that exploits image dissimilarities is proposed, treating re-identification as a binary classification problem. To achieve the objective, the person re-identification problem is addressed as follows: (i) first, compute the image dissimilarity between a pair of images acquired from two disjoint cameras; (ii) then learn the linear subspace where the image dissimilarities lie in an unsupervised fashion and (iii) lastly train a binary classifier in the linear subspace to discriminate between image dissimilarities computed for a positive pair (images are for the same person) and a negative pair (images are for different persons). An approach on two publicly available benchmark datasets is evaluated and compared with state-of-the-art methods for person re-identification. © The Institution of Engineering and Technology 2014.
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/11390/1036553
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 4
social impact