%0 Journal Article %T A New Model for Person Reidentification Using Deep CNN and Autoencoders %J Iranica Journal of Energy & Environment %I Babol Noshirvani University of Technology %Z 2079-2115 %A Sezavar, A. %A Farsi, H. %A Mohamadzadeh, S. %D 2023 %\ 10/01/2023 %V 14 %N 4 %P 314-320 %! A New Model for Person Reidentification Using Deep CNN and Autoencoders %K auto-encoder %K Deep Learning %K Image Hashing %K person re-identification %R 10.5829/ijee.2023.14.04.01 %X 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. %U https://www.ijee.net/article_168788_10a725b84ea3f98ebcff32242c885c7e.pdf