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http://dx.doi.org/10.9717/kmms.2020.23.8.891

Deep Neural Networks Learning based on Multiple Loss Functions for Both Person and Vehicles Re-Identification  

Kim, Kyeong Tae (Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies)
Choi, Jae Young (Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies)
Publication Information
Journal of Korea Multimedia Society / v.23, no.8, 2020 , pp. 891-902 More about this Journal
Abstract
The Re-Identification(Re-ID) is one of the most popular researches in the field of computer vision due to a variety of applications. To achieve a high-level re-identification performance, recently other methods have developed the deep learning based networks that are specialized for only person or vehicle. However, most of the current methods are difficult to be used in real-world applications that require re-identification of both person and vehicle at the same time. To overcome this limitation, this paper proposes a deep neural network learning method that combines triplet and softmax loss to improve performance and re-identify people and vehicles simultaneously. It's possible to learn the detailed difference between the identities(IDs) by combining the softmax loss with the triplet loss. In addition, weights are devised to avoid bias in one-side loss when combining. We used Market-1501 and DukeMTMC-reID datasets, which are frequently used to evaluate person re-identification experiments. Moreover, the vehicle re-identification experiment was evaluated by using VeRi-776 and VehicleID datasets. Since the proposed method does not designed for a neural network specialized for a specific object, it can re-identify simultaneously both person and vehicle. To demonstrate this, an experiment was performed by using a person and vehicle re-identification dataset together.
Keywords
Re-Identification; Person; Vehicle; Combined Loss; Convolutional Neural Network;
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