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:
this https URL

本研究主要研究基于视频的跨模态人员重新识别方法。通过构建一个视频 RGB-IR 数据集，证明了在 RGB-IR 人员重新识别中，视频到视频的匹配非常重要。此外，还提出了一种新的方法，该方法不仅将两种模态投射到模态不变的子空间中，还提取了时间内存以实现运动不变性。