a new model for person reidentification using deep cnn and autoencoders

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.