The person re-identification problem, i.e. recognizing a person across non-overlapping cameras at different times and locations, is of fundamental importance for video surveillance applications. Due to pose variations, illumination conditions, background clutter, and occlusions, re-identify a person is an inherently difficult problem which is still far from being solved. In this work, inspired by the recent police lineup innovations, we propose a re-identification approach where Multiple Re-identification Experts (MuRE) are trained to reliably match new probes. The answers from all the experts are then combined to achieve a final decision. The proposed method has been evaluated on three datasets showing significant improvements over state-of-the-art approaches. © 2015 Elsevier B.V.All rights reserved.
A pool of multiple person re-identification experts / Martinel, Niki; Micheloni, Christian; Foresti, Gian Luca. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - STAMPA. - 71:February(2016), pp. 23-30.
Titolo: | A pool of multiple person re-identification experts |
Autori: | |
Data di pubblicazione: | 2016 |
Rivista: | |
Citazione: | A pool of multiple person re-identification experts / Martinel, Niki; Micheloni, Christian; Foresti, Gian Luca. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - STAMPA. - 71:February(2016), pp. 23-30. |
Abstract: | The person re-identification problem, i.e. recognizing a person across non-overlapping cameras at different times and locations, is of fundamental importance for video surveillance applications. Due to pose variations, illumination conditions, background clutter, and occlusions, re-identify a person is an inherently difficult problem which is still far from being solved. In this work, inspired by the recent police lineup innovations, we propose a re-identification approach where Multiple Re-identification Experts (MuRE) are trained to reliably match new probes. The answers from all the experts are then combined to achieve a final decision. The proposed method has been evaluated on three datasets showing significant improvements over state-of-the-art approaches. © 2015 Elsevier B.V.All rights reserved. |
Handle: | http://hdl.handle.net/11390/1087184 |
Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
File | Descrizione | Tipologia | Licenza | |
---|---|---|---|---|
PRL2015.pdf | Documento in Post-print | Non pubblico | Accesso ristretto Richiedi una copia |