Part-Attention Based Model Make Occluded Person Re-Identification Stronger
This work addresses the problem of retrieving pedestrians in occluded scenarios for surveillance and security applications, representing an incremental improvement over existing methods.
The paper tackles occluded person re-identification by introducing PAB-ReID, a framework using part-attention mechanisms to improve feature representations and reduce background clutter, achieving state-of-the-art performance on specialized occlusion and regular datasets.
The goal of occluded person re-identification (ReID) is to retrieve specific pedestrians in occluded situations. However, occluded person ReID still suffers from background clutter and low-quality local feature representations, which limits model performance. In our research, we introduce a new framework called PAB-ReID, which is a novel ReID model incorporating part-attention mechanisms to tackle the aforementioned issues effectively. Firstly, we introduce the human parsing label to guide the generation of more accurate human part attention maps. In addition, we propose a fine-grained feature focuser for generating fine-grained human local feature representations while suppressing background interference. Moreover, We also design a part triplet loss to supervise the learning of human local features, which optimizes intra/inter-class distance. We conducted extensive experiments on specialized occlusion and regular ReID datasets, showcasing that our approach outperforms the existing state-of-the-art methods.