  Most of the proposed person re-identification algorithms conduct supervised
training and testing on single labeled datasets with small size, so directly
deploying these trained models to a large-scale real-world camera network may
lead to poor performance due to underfitting. It is challenging to
incrementally optimize the models by using the abundant unlabeled data
collected from the target domain. To address this challenge, we propose an
unsupervised incremental learning algorithm, TFusion, which is aided by the
transfer learning of the pedestrians' spatio-temporal patterns in the target
domain. Specifically, the algorithm firstly transfers the visual classifier
trained from small labeled source dataset to the unlabeled target dataset so as
to learn the pedestrians' spatial-temporal patterns. Secondly, a Bayesian
fusion model is proposed to combine the learned spatio-temporal patterns with
visual features to achieve a significantly improved classifier. Finally, we
propose a learning-to-rank based mutual promotion procedure to incrementally
optimize the classifiers based on the unlabeled data in the target domain.
Comprehensive experiments based on multiple real surveillance datasets are
conducted, and the results show that our algorithm gains significant
improvement compared with the state-of-art cross-dataset unsupervised person
re-identification algorithms.
