Part Uncertainty Estimation Convolutional Neural Network For Person Re-Identification

Due to the large amount of noisy data in person re-identification (ReID) task, the ReID models are usually affected by the data uncertainty. Therefore, the deep uncertainty estimation method is important for improving the model robustness and matching accuracy. To this end, we propose a part-based uncertainty convolutional neural network (PUCNN), which introduces the part-based uncertainty estimation into the baseline model. On the one hand, PUCNN improves the model robustness to noisy data by distributilizing the feature embedding and constraining the part-based uncertainty. On the other hand, PUCNN improves the cumulative matching characteristics (CMC) performance of the model by filtering out low-quality training samples according to the estimated uncertainty score. The experiments on both non-video datasets, the noised Market-1501 and DukeMTMC, and video datasets, PRID2011, iLiDS-VID and MARS, demonstrate that our proposed method achieves encouraging and promising performance.