仅用事件数据实现高精度行人重识别，兼顾隐私与运动信息
Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration
- 设计事件变换网络提取原始事件中的辅助信息
- 通过跨模态协同模块平衡事件与辅助信息，提升互补性
- 引入时序协同模块挖掘运动与外观特征，适合隐私敏感场景
基于视频的行人重识别（ReID）在视频监控中日益重要。利用事件数据可提供连续帧间的更多运动信息，从而提升识别准确率。以往方法虽引入事件数据，但仍存在由RGB图像引发的隐私泄露问题。为避免隐私攻击并充分利用事件数据优势，本文仅使用事件数据，提出一种跨模态与时序协同（CMTC）网络用于事件驱动的视频行人重识别。首先设计事件变换网络，从原始事件流中提取辅助信息；其次提出差异模态协同模块，动态平衡事件与辅助信息的作用，实现互补；最后引入时序协同模块，有效利用运动信息与外观线索。实验表明，该方法在事件驱动的视频行人重识别任务中优于现有方法。
原文摘要 · Abstract (English)
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.
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