A New Model for Person Reidentification Using Deep CNN and Autoencoders

Abstract

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.