Person re-identification is a prominent topic in computer vision due to its security-related applications, and to the fact that issues such as variations in illumination, background, pedestrian pose and clothing appearance make it a very challenging task in real-world scenarios. State-of-the-art supervised methods require a huge manual annotation effort for training data and exhibit limited generalisation capability to unknown target domains. Synthetic data sets have recently been proposed as one possible solution to mitigate these problems, aimed at improving generalisation capability by encompassing a larger amount of variations in the above mentioned visual factors, with no need for manual annotation. However, existing synthetic data sets differ in many aspects, including the number of images, identities and cameras, and in their degree of photorealism, and there is not yet a clear understanding of how all such factors affect person re-identification performance. This work makes a first step towards filling this gap through an in-depth empirical investigation, where we use existing synthetic data sets for model training and real benchmark ones for performance evaluation. Our results provide interesting insights towards developing effective synthetic data sets for person re-identification.

On the effectiveness of synthetic data sets for training person re-identification models

Delussu, R
Primo
;
Putzu, L
Secondo
;
Fumera, G
Ultimo
2022-01-01

Abstract

Person re-identification is a prominent topic in computer vision due to its security-related applications, and to the fact that issues such as variations in illumination, background, pedestrian pose and clothing appearance make it a very challenging task in real-world scenarios. State-of-the-art supervised methods require a huge manual annotation effort for training data and exhibit limited generalisation capability to unknown target domains. Synthetic data sets have recently been proposed as one possible solution to mitigate these problems, aimed at improving generalisation capability by encompassing a larger amount of variations in the above mentioned visual factors, with no need for manual annotation. However, existing synthetic data sets differ in many aspects, including the number of images, identities and cameras, and in their degree of photorealism, and there is not yet a clear understanding of how all such factors affect person re-identification performance. This work makes a first step towards filling this gap through an in-depth empirical investigation, where we use existing synthetic data sets for model training and real benchmark ones for performance evaluation. Our results provide interesting insights towards developing effective synthetic data sets for person re-identification.
2022
978-1-6654-9062-7
978-1-6654-9063-4
Benchmarking; Clothing appearances; Generalization capability; Identification modeling; Manual annotation; Person re identifications; Real-world scenario; State of the art; Supervised methods; Synthetic datasets; Training data; Computer vision
File in questo prodotto:
File Dimensione Formato  
ICPR 2022.pdf

Solo gestori archivio

Descrizione: VoR
Tipologia: versione editoriale (VoR)
Dimensione 1.26 MB
Formato Adobe PDF
1.26 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
paper_final.pdf

accesso aperto

Descrizione: AAM
Tipologia: versione post-print (AAM)
Dimensione 756.72 kB
Formato Adobe PDF
756.72 kB Adobe PDF Visualizza/Apri

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/11584/361264
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact