Video-based person re-identification has drawn massive attention in recent years due to its extensive applications in video surveillance. While deep learning-based methods have led to significant progress, these methods are limited by ineffectively using complementary information, which is blamed on necessary data augmentation in the training process. Data augmentation has been widely used to mitigate the over-fitting trap and improve the ability of network representation. However, the previous methods adopt image-based data augmentation scheme to individually process the input frames, which corrupts the complementary information between consecutive frames and causes performance degradation. Extensive experiments on three benchmark datasets demonstrate that our framework outperforms the most recent state-of-the-art methods. We also perform cross-dataset validation to prove the generality of our method.
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