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EnglishThe 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 |
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http://hdl.handle.net/11390/1087184
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