Abstract
The quadratic discriminant analysis (XQDA) method learns a general projection matrix for all cameras with its strong generalization ability, but it ignores the inherent properties of each camera itself and does not take feature of changes in each camera into account, causing each person under the camera to have a certain feature distortion problem which makes its discriminative ability worse. In this paper, feature augmentation is used to enhance the inherent properties of each camera. By ensuring the generalization ability of the camera, the feature of changes within each camera is taken into consideration and the final discriminative ability is improved. Finally, experiments on a challenging person re-identification dataset, VIPeR, show that the proposed method outperforms the state-of-the-art methods.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under Grant no. 61402237, 61502245, 61772568 and the Natural Science Foundation of Jiangsu Province under Grant no. BK20150849. Guangwei Gao is the corresponding author.
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Wang, C., Qi, H., Gao, G., Jing, X. (2020). Quadratic Discriminant Analysis Metric Learning Based on Feature Augmentation for Person Re-Identification. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_18
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