Semantically enhanced attention map‐driven occluded person re‐identification

Occluded person re‐identification (Re‐ID) is to identify a particular person when the person's body parts are occluded. However, challenges remain in enhancing effective information representation and suppressing background clutter when considering occlusion scenes. This paper proposes a novel attention map‐driven network (AMD‐Net) for occluded person Re‐ID. In AMD‐Net, human parsing labels are introduced to supervise the generation of partial attention maps, while a spatial‐frequency interaction module is suggested to complement the higher‐order semantic information from the frequency domain. Furthermore, a Taylor‐inspired feature filter for mitigating background disturbance and extracting fine‐grained features is proposed. Moreover, a part‐soft triplet loss, which is robust to non‐discriminative body partial features is also designed. Experimental results on Occluded‐Duke, Occluded‐Reid, Market‐1501, and Duke‐MTMC datasets show that this method outperforms existing state‐of‐the‐art methods. The code is available at: https://github.com/ISCLab‐Bistu/SA‐ReID. This paper proposes an attention map‐driven network (AMD‐Net) for occluded person Re‐ID. To begin with, human parsing labels are utilized to establish more precise feature extraction regions. Subsequently, the spatial‐frequency interaction module and the Taylor‐inspired feature filter are introduced to add valid information and suppress background clutter. Lastly, a part‐soft triplet loss is suggested to increase the model's inclusiveness of the non‐discriminative body partial features.