Most existing person re-identification (ReID) methods assume the availability of extensively labelled cross-view person pairs and a closed-set scenario (i.e. all the probe people exist in the gallery set). These two assumptions significantly limit their usefulness and scalability in real-world applications, particularly with large scale camera networks. To overcome the limitations, we introduce a more challenging yet realistic ReID setting termed OneShot-OpenSet-ReID, and propose a novel Regularised Kernel Subspace Learning model for ReID under this setting. Our model differs significantly from existing ReID methods due to its ability of effectively learning cross-view identity-specific information from unlabelled data alone, and its flexibility of naturally accommodating pairwise labels if available.
Wang et al. REF propose a kernel-based model to learn cross-view identity discriminative information from unlabeled data.
Towards unsupervised open-set person re-identification
2016 IEEE International Conference on Image Processing (ICIP)
2016 IEEE International Conference on Image Processing (ICIP)
Computer Science