Intra-Clip Aggregation For Video Person Re-Identification

Abstract

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 training process. Data augmentation has been widely used to mitigate the overfitting 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. In this paper, we propose a novel video-based data augmentation scheme, termed as Synchronous Data Augmentation, to address the challenge above. In order to represent discriminative clip-level features, we also propose a cascade integration module which hierarchically aggregates the intra-clip features with a linear-nonlinear combining projection. 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.