论文标题
学习基于视频的可见红外人员重新识别的模态和时间记忆
Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification
论文作者
论文摘要
感谢您的跨模式检索技术，通过将它们投射到一个共同的空间中，可以实现可见的信号（RGB-IR）重新识别（RE-ID），从而使人在24小时监视系统中重新进行重新ID。但是，关于探针到探测器，几乎所有现有的基于RGB-IR的跨模式的人重新ID方法都集中在图像到图像匹配上，而视频对视频匹配的匹配包含更丰富的空间和时间信息，则仍然不足。在本文中，我们主要研究基于视频的跨模式人Re-ID方法。为了实现这项任务，构建了一个基于视频的RGB-IR数据集，其中927个有效身份，具有463,259帧和21,863个曲目，由12个RGB/IR摄像头捕获。基于我们构造的数据集，我们证明，随着曲目中帧的增加，该性能确实达到了更多的增强功能，这证明了在RGB-ir Re-ID中视频与视频匹配的重要性。此外，进一步提出了一种新颖的方法，不仅将两种模态投射到模态不变的子空间，而且还提取了运动不变的时间记忆。得益于这两种策略，我们基于视频的跨模式人重新ID取得了更好的结果。代码和数据集以：https：//github.com/vcmproject233/mitml发布。
Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the probe-to-gallery, almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal person Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code and dataset are released at: https://github.com/VCMproject233/MITML.
