Abstract
Video-based person re-identification (ReID) has become increasingly important due to its applications in video surveillance applications. By employing events in video-based person ReID, more motion information can be provided between continuous frames to improve recognition accuracy. Previous approaches have assisted by introducing event data into the video person ReID task, but they still cannot avoid the privacy leakage problem caused by RGB images. In order to avoid privacy attacks and to take advantage of the benefits of event data, we consider using only event data. To make full use of the information in the event stream, we propose a Cross-Modality and Temporal Collaboration (CMTC) network for event-based video person ReID. First, we design an event transform network to obtain corresponding auxiliary information from the input of raw events. Additionally, we propose a differential modality collaboration module to balance the roles of events and auxiliaries to achieve complementary effects. Furthermore, we introduce a temporal collaboration module to exploit motion information and appearance cues. Experimental results demonstrate that our method outperforms others in the task of event-based video person ReID.
Abstract (translated)
基于视频的人重识别（ReID）由于在视频监控中的应用而变得越来越重要。通过采用事件数据，在连续帧之间可以提供更多的运动信息，从而提高识别的准确性。虽然之前的方法已经尝试将事件数据引入到基于视频的人体重识别任务中来辅助解决该问题，但它们仍然无法避免由RGB图像引起的隐私泄露问题。为了防止隐私攻击，并利用事件数据带来的优势，我们考虑仅使用事件数据进行人体重识别。 为充分利用事件流中的信息，我们提出了一种跨模态和时间协作（CMTC）网络用于基于事件的视频人体重识别任务。首先，我们设计了一个事件变换网络来从原始事件输入中获取相应的辅助信息。此外，我们提出了一个差分模态协作模块以平衡事件与辅助信息的作用，从而实现互补效果。同时，我们引入了时间协作模块来利用运动信息和外观线索。 实验结果表明，在基于事件的视频人体重识别任务上，我们的方法优于其他现有技术。
URL
https://arxiv.org/abs/2501.07296
