Une approche structurelle pour la ré-identification de personnes
| Autor: | Luc Brun, Donatelo Conte, Amal Mahboubi |
|---|---|
| Přispěvatelé: | Mahboubi, Amal, Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA) |
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Scale (chemistry) Feature extraction [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering 02 engineering and technology Tracking (particle physics) Machine learning computer.software_genre Object (philosophy) ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.10: Vision and Scene Understanding [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] Kernel (image processing) [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Video tracking 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Artificial intelligence business computer ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning |
| Zdroj: | ICPR 24th International Conference on Pattern Recognition (ICPR) 24th International Conference on Pattern Recognition (ICPR), Aug 2018, Pékin, China. pp.1616-1621 ICPR 2018 International Conference on Pattern Recognition ICPR 2018 International Conference on Pattern Recognition, Aug 2018, Beijing, China |
| DOI: | 10.1109/icpr.2018.8545640 |
| Popis: | International audience; Although it has been studied extensively during past decades, object tracking is still a difficult problem due to many challenges. Several improvements have been done, but more and more complex scenes (dense crowd, complex interactions) need more sophisticated approaches. Particularly long-term tracking is an interesting problem that allow to track objects even after it may become longtime occluded or it leave/re-enter the field-of-view. In this case the major challenges are significantly changes in appearance, scale and so on. At the heart of the solution of long-term tracking is the re-identification technique, that allows to identify an object coming back visible after an occlusion or re-entering on the scene. This paper proposes an approach for pedestrian re-identification based on structural representation of people. The experimental evaluation is carried out on two public data sets (ETHZ and CAVIAR4REID datasets) and they show promising results compared to others state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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