A New Model for Person Reidentification Using Deep CNN and Autoencoders
| Autor: | A. Sezavar, H. Farsi, S. Mohamadzadeh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Iranica Journal of Energy and Environment, Vol 14, Iss 4, Pp 314-320 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2079-2115 2079-2123 |
| DOI: | 10.5829/ijee.2023.14.04.01 |
| Popis: | Person re-identification (re-id) is one of the most critical and challenging topics in image processing and artificial intelligence. In general, person re-identification means that a person seen in the field of view of one camera can be found and tracked by other non-overlapped cameras. Low-resolution frames, high occlusion in crowded scene, and few samples for training supervised models make re-id challenging. This paper proposes a new model for person re-identification to overcome the noisy frames and extract robust features from each frame. To this end, a noise-aware system is implemented by training an auto-encoder on artificially damaged frames to overcome noise and occlusion. A model for person re-identification is implemented based on deep convolutional neural networks. Experimental results on two actual databases, CUHK01 and CUHK03, demonstrate that the proposed method performs better than state-of-the-art methods. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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