Top-Down Person Re-Identification With Siamese Convolutional Neural Networks

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2018 International Joint Conference on Neural Networks (IJCNN), 2018
Issue Date:
2018-07
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Automated person re-identification is a challenging research problem that has many real-world applications, especially in video surveillance. While many recent studies have been focusing on solving the person re-identification problem using full-scale images or video footages, little work has been done to solve the person re-identification problem in a top-down context. In this work, we propose a solution to the top-down reidentification problem that uses the Siamese architecture in conjunction with Convolutional Neural Networks. In our approach, a pair of top-down images is distinguished by a single Siamese network, which is trained to predict the similarity, or a distance between two input images. Experiments have shown that once the model is properly trained, it is able to achieve one-shot, top-down re-identification by learning unseen classes of person in real-time.
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