Abstract
The field of person re-identification remains a challenging topic in video surveillance and public security because it is facing many problems related to the variations of the position, background and brightness scenes. In order to minimize the impact of those variations, we introduce in this work a multi-stream re-identification system based on the fusion of local and global features. The proposed system uses first a body partition segmentation network (SEG-CNN) to segment three different body regions (the whole body part, the middle and the down body parts) that will represent local features. While the original image will be used to extract global features. Second, a multi-stream fusion framework is performed to fuse the outputs of the individual streams and generate the final predictions. We experimentally prove that the multi-stream combination method improves the recognition rates and provides better results than classic fusion methods. In the rank-1/mAP, the improvement is of \(7,24 \%\)/9, 5 for the Market-1501 benchmark dataset.
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Acknowledgement
This project is carried out under the MOBIDOC scheme, funded by the EU through the EMORI program and managed by the ANPR. We thank Anavid for assistance. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
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Ghorbel, M., Ammar, S., Kessentini, Y., Jmaiel, M., Chaari, A. (2021). Fusing Local and Global Features for Person Re-identification Using Multi-stream Deep Neural Networks. In: Djeddi, C., Kessentini, Y., Siddiqi, I., Jmaiel, M. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2020. Communications in Computer and Information Science, vol 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-71804-6_6
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