Mutual prediction learning and mixed viewpoints for unsupervised-domain adaptation person re-identification on blockchain

In addition to the domain shift between different datasets, the diversity of pedestrian appearance (physical appearance and postures) caused by different camera views also affects the performance of person re-identification (re-ID). Since existing methods tend to extract the shared information of the same pedestrian across multiple images, the above diversity issue has not been effectively alleviated. In addition, while making full use of pedestrian image data and realizing its value, there are also risks of privacy leakage and data loss. Therefore, this paper proposes the mutual prediction learning (MPL) and mixed viewpoints for unsupervised domain adaptation (UDA) person re-ID on blockchain. This method enables the network to first obtain the ability of MPL on multi-view polymorphic features and further acquire the reasoning imagination to alleviate the ambiguity caused by morphological differences. In the process of MPL, the training samples are first divided into different groups and each group has two sets. Then the corresponding identity classifiers of every two sets are integrated and applied to the cross-prediction of polymorphic features. Finally, the joint distribution alignment of domain-and identity-level features is achieved. Furthermore, an adversarial mechanism of mixed viewpoints is proposed to improve the accuracy of identity matching. The domain invariant salient features are extracted and fused with the polymorphic features obtained by global average pooling (GAP) after domain alignment. Thanks to blockchain technology, the pedestrian image data of the data owner is also protected. Comparative experimental results confirm the effectiveness of the proposed solution in person re-ID. The related source codes will be available at: https://github.com/lhf12278/MPL-MV.