Abstract Most of the proposed person re-identifification 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 underfifitting. 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. Specififically, the algorithm fifirstly transfers the visual classififier 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 signifificantly improved classififier. Finally, we propose a learning-to-rank based mutual promotion procedure to incrementally optimize the classififiers 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 signifificant improvement compared with the state-of-art cross-dataset unsupervised person reidentifification algorithms