Person Re-identification Using Convolutional Neural Networks

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2021-9-6

Author

Aksu, Fatih

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Person re-identification is an important computer vision topic particularly for surveillance system applications. The main aim of person re-identification is to identify previously observed individuals over a camera network with nonoverlapping occurrences. Various approaches have been introduced to overcome this problem. This study has two distinct key contributions. First, a new part-based method for person re-identification, which combines semantically partitioned body part masks with a convolutional neural network, is proposed. With this study, it is observed that the proposed body part-based system has promising results, and has advantages over the baseline person re-identification systems. The second contribution of this study is to investigate and compare potential lightweight convolutional neural networks suitable for person re-identification task. Results show that some lightweight convolutional neural networks can be used instead of more generalized deeper networks. Well-designed lightweight convolutional neural networks may have higher accuracy with lower computational cost for person reidentification.

Subject Keywords

Person re-identification, Semantic segmentation, Lightweight convolutional neural networks

URI

https://hdl.handle.net/11511/94270

Collections

Northern Cyprus Campus, Thesis

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Citation Formats

F. Aksu, “Person Re-identification Using Convolutional Neural Networks,” M.S. - Master of Science, Middle East Technical University, 2021.