Person re-identification(Re-ID) aims to retrieve pedestrians under different cameras. Compared with image-based Re-ID, video-based Re-ID extracts features from video sequences that contain both spatial features and temporal features. Existing methods usually focus on the most attractive image parts, and this will lead to redundant spatial description and insufficient temporal description. Other methods that take temporal clues into consideration usually ignore misalignment between frames and only focus on a fixed length of one given sequence. In this study, we proposed a Reciprocal Global Temporal Convolution Network with Adaptive Alignment(AA-RGTCN). The structure could address the drawback of misalignment between frames and model discriminative temporal representation. Specifically, the Adaptive Alignment block is designed to shift each frame adaptively to its best position for temporal modeling. Then, we proposed the Reciprocal Global Temporal Convolution Network to model robust temporal features across different time intervals along both normal and inverted time order. The experimental results show that our AA-RGTCN can achieve 85.9% mAP and 91.0% Rank-1 on MARS, 90.6% Rank-1 on iLIDS-VID, and 96.6% Rank-1 on PRID-2011, indicating we could gain better performance than other state-of-the-art approaches.

中文翻译：

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AA-RGTCN：具有自适应对齐功能的互易全局时间卷积网络，用于基于视频的行人重新识别

行人重识别（Re-ID）旨在检索不同摄像头下的行人。与基于图像的Re-ID相比，基于视频的Re-ID从视频序列中提取同时包含空间特征和时间特征的特征。现有的方法通常关注最吸引人的图像部分，这会导致冗余的空间描述和不充分的时间描述。其他考虑时间线索的方法通常会忽略帧之间的错位，而只关注一个给定序列的固定长度。在这项研究中，我们提出了一种具有自适应对齐的倒数全局时间卷积网络（AA-RGTCN）。该结构可以解决帧和模型判别时间表示之间未对准的缺点。具体来说，自适应对齐模块旨在将每个帧自适应地移动到时间建模的最佳位置。然后，我们提出了倒数全局时间卷积网络，以沿正常和反转时间顺序对不同时间间隔内的鲁棒时间特征进行建模。实验结果表明，我们的AA-RGTCN在MARS上可以达到85.9％的mAP和91.0％的Rank-1，在iLIDS-VID上可以达到90.6％的Rank-1，在PRID-2011上可以达到96.6％的Rank-1，这表明我们可以获得更好的性能比其他最先进的方法。