Person re-identification plays a central role in tracking and monitoring crowd movement in public places, and hence it serves as an important means for providing public security in video surveillance application sites. The problem of person re-identification has received significant attention in the past few years, and with the introduction of deep learning, several interesting approaches have been developed. In this paper, we propose an ensemble model called Temporal Motion Aware Network (T-MAN) for handling the visual context and spatio-temporal information jointly from the input video sequences. Our methodology makes use of the long-range motion context with recurrent information for establishing correspondences among multiple cameras. The proposed T-MAN approach first extracts explicit frame-level feature descriptors from a given video sequence by using three different sub-networks (FPAN, MPN, and LSTM), and then aggregates these models using an ensemble technique to perform re-identification. The method has been evaluated on three publicly available data sets, namely, the PRID-2011, iLIDS-VID, and MARS, and re-identification accuracy of 83.0%, 73.5%, and 83.3% have been obtained from these three data sets, respectively. Experimental results emphasize the effectiveness of our approach and its superiority over the state-of-the-art techniques for video-based person re-identification.

人员重新识别在跟踪和监视公共场所人群的移动中起着核心作用，因此，它是在视频监视应用程序站点中提供公共安全的重要手段。在过去的几年中，重新识别人的问题受到了极大的关注，并且随着深度学习的引入，已经开发了几种有趣的方法。在本文中，我们提出了一个称为时间运动感知网络（T-MAN）的集成模型，用于共同处理来自输入视频序列的视觉上下文和时空信息。我们的方法利用远程运动上下文和循环信息在多个摄像机之间建立对应关系。FPAN，MPN和LSTM），然后使用集成技术汇总这些模型以执行重新标识。该方法已在PRID-2011，iLIDS-VID和MARS的三个公开数据集上进行了评估，并且从这三个数据集获得的重识别准确度分别为83.0％，73.5％和83.3％，分别。实验结果强调了我们方法的有效性及其相对于基于视频的人员重新识别的最新技术的优越性。