Abstract
Person re-identification is yet a critical challenging task in video surveillance domain. It aims to match the same person across different cameras. Practically, pedestrian’s appearances may vary greatly due to the complex background. Most deep learning methods rely on convolutional neural network to extract the feature of the pedestrian. But most of them lose the crucial details of the pedestrian and are sensitive to the viewpoints of the camera. To remedy this problem, we propose using capsule network as the feature extractor and introduce an improved loss function for the network. The experiment results on the Market-1501 dataset show the effectiveness of the proposed method.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61672203, 61572447, 61772357, 31571364, 61861146002,61520106006, 61772370, 61702371, 61672382, and 61732012, China Post-doctoral Science Foundation Grant, No. 2017M611619, and supported by “BAGUI Scholar” Program and the Scientific & Technological Base and Talent Special Program, GuiKe AD18126015 of the Guangxi Zhuang Autonomous Region of China.
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Li, A., Wu, D., Huang, DS., Zhang, L. (2019). Convolutional Capsule-Based Network for Person Re-identification. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_29
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