Re-identification of Occluded Pedestrian Based on Local Representation Learning Through Feature Fusion
To enhance the occlusion perception and local feature capturing ability of pedestrian re-identification(ReID)models,this paper proposes a method of local representation learning based on feature fusion.First,an Occlusion Sample Expansion Strategy(OSES)is designed to effectively improve the model's robustness and occlusion perception ability by simulating diverse occlusion scenarios.Second,a local hierarchical encoder is introduced to extract spatial correlation features of the sequence under the guidance of global semantics,thereby enhancing the discriminability and semantic integrity of local features.Experimental results on the Occluded-Duke and Market-1501 datasets demonstrate the effectiveness of the method.The rank-1 on the Occluded-Duke dataset reaches 69.2%,which outperforms the existing state-of-the-art methods by 1.3 percentage points and improves the performance of the re-recognition task.
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