Min Huang
ABSTRACT
Person re-identification can quickly locate and find all the specified targets in complex scenes with multiple cameras, which has been widely applied in intelligent video surveillance and security system. As a state-of-art method proposed recently, ResNet exhibits promising performances on person re-identification. However, without intermediate fully connected layer, ResNet fails to fully grasp the global information in the detection process. To overcome the above problem, this paper proposes a person re-identification method named RG-BoTNet by fusing the Relation-aware Global Attention mechanism into BoTNet. Since relation-aware global attention is good at grasping the global information of the image, RG-BoTNet is powerful in extracting personal features. The good performances conducted on cuhk03 dataset in terms of Mean Average Precision (MAP) and Rank-1demonstrate the effectiveness of RG-BoTNet for person re-identification task.
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Index Terms
- A Person Re-identification Method Fusing Bottleneck Transformer andRelation-aware Global Attention
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