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The progressive clustering method and its variants, which iteratively generate pseudo labels for unlabeled data and per form feature learning, have shown great process in unsupervised person re-identification (re-id). However, they have an intrinsic problem of modeling the in-camera variability of images successfully, that is, pedestrian features extracted from the same camera tend to be clustered into the same class. This often results in a non-convergent model in the real world application of clustering based re-id models, leading to degenerated performance. In the present study, we propose an attention-driven two-stage clustering (ADTC) method to solve this problem. Specifically, our method consists of two strategies. Firstly, we use an unsupervised attention kernel to shift the learned features from the image background to the pedestrian foreground, which results in more informative clusters. Secondly, to …