Occluded Person Reidentification via a Universal Framework With Difference Consistency Guidance Learning
Yuxuan Liu, Hongwei Ge, Guozhi Tang, Yong Cheng Luo · IEEE Internet of Things Journal · 2024
Occluded person reidentification (Re-ID) aims at learning discriminative identity features to match person images under the interference of occlusion situations in video surveillance of the visual Internet of Things (VIoT). Currently, occluded person Re-ID methods have made impressive improvements in nonperson occlusion situations. However, in real-world scenarios, the target person is commonly occluded by other nontarget persons, and the fine-grained differences between the persons make the model difficult to distinguish the discriminative identity features. To this end, we propose a difference consistency guidance (DCG) learning to enlarge the fine-grained differences by the guidance of the coarse-grained differences, which can distinguish the discriminative identity features in nontarget person occlusion situations. Then, DCG reduces the identity feature representation of the nonperson occlusion instances, which further improves the ability of the model in nonperson occlusion situations and improves the guidance ability of coarse-grained difference. Moreover, DCG can enhance the robustness of the model in the unsupervised occluded person Re-ID task and further improve the universal applicability of the model. Extensive experiment results under the supervised and unsupervised settings demonstrate the DCG outperforms the state-of-the-art methods in experiments conducted on the occluded person Re-ID benchmarks.