Embedding Deep Metric for Person Re-identification: A Study Against Large Variations

Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)’s capability of feature extraction.

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Hailin Shi, Yang Yang, Xiangyu Zhu, Shengcai Liao, Zhen Lei, Weishi Zheng, Stan Z. Li (2024). Dataset: Embedding Deep Metric for Person Re-identification: A Study Against Large Variations. https://doi.org/10.57702/ie18gd07

DOI retrieved: December 16, 2024

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Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.1611.00137
Author Hailin Shi
More Authors
Yang Yang
Xiangyu Zhu
Shengcai Liao
Zhen Lei
Weishi Zheng
Stan Z. Li