Abstract
Person re-identification is a popular topic in computer vision, aiming to retrieve a given pedestrian image across the camera. In this paper, a new Person re-identification based on camera style (CamStyle) adaptation is proposed to solve the problem of lack of data and lack of information in pedestrian feature extraction. In the stage of image preprocessing, CamStyle can serve as a data augmentation approach that transforms the camera style of image. In the training stage, using ID loss and Triplet loss to supervise training of eigenvectors. The experimental results show that the recognition accuracy of the method is improved greatly on Market1501, and the validity of the method is verified.
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Acknowledgments
This research is supported by National Key Research and Development Scheme of China under grant number 2017YFC1405403, and National Natural Science Foundation of China under grant number 61075059, and Green Industry Technology Leading Project (product development category) of Hubei University of Technology under grant number CPYF2017008.
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Zhang, T., Xiong, C. (2020). Person Re-identification Based on Camera Style Adaptation. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_45
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DOI: https://doi.org/10.1007/978-3-030-44038-1_45
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