Graph based Spatial-temporal Fusion for Multi-modal Person Re-identification

As a challenging task, unsupervised person re-identification (Re-ID) aims to optimize the pedestrian matching model based on the unlabeled image frames from surveillance videos. Recently, the fusion with the spatio-temporal clues of pedestrians have been proven effective to improve the performance of classification. However, most of these methods adopt some hard combination approaches by multiplying the visual scores with the spatio-temporal scores, which are sensitive to the noise caused by imprecise estimation of the spatio-temporal patterns in unlabeled datasets and limit the advantage of the fusion model. In this paper, we propose a Graph based Spatio-Temporal Fusion model for high-performance multi-modal person Re-ID, namely G-Fusion, to mitigate the impact of noise. In particular, we construct a graph of pedestrian images by selecting neighboring nodes based on the visual information and the transition time between cameras. Then we use a randomly initialized two-layer GraphSAGE model to obtain the multi-modal affinity matrix between images, and deploy the distillation learning to optimize the visual model by learning the affinity between the nodes. Finally, a graph-based multi-modal re-ranking method is deployed to make the decision in the testing phase for precise person Re-ID. Comprehensive experiments are conducted on two large-scale Re-ID datasets, and the results show that our method achieves a significant improvement of the performance while combined with SOTA unsupervised person Re-ID methods. Specifically, the mAP scores can reach 92.2%, and 80.4% on the Market-1501, and MSMT17 datasets respectively.