Video-based Visible-Event Cross-modal Person Re-identification for Edge AI Surveillance Systems

Video-based cross-modal person re-identification (ReID) is a critical task for video surveillance and security systems, particularly in resource-constrained edge AI environments. While existing crossmodal ReID methods primarily focus on thermal-visible matching, event cameras, with their low power consumption, high temporal resolution, and sparse data representation, offer significant advantages for edgebased surveillance systems by reducing data processing overhead and enabling robust performance under challenging lighting conditions. In this paper, we introduce a novel task: video-based visible-event person re-identification (VE ReID), which aims to match identities across RGB and event camera modalities. To the best of our knowledge, this is the first work to systematically define and investigate this cross-modal task in the context of event-driven edge AI. Specifically, we curate evaluation benchmarks from existing RGB-event datasets and synthesize a new RGB-event dataset, explicitly adapting them to the cross-modal ReID setting to enable a more comprehensive evaluation of VE ReID. Extensive experiments reveal that existing cross-modal state-of-the-art (SOTA) methods fail to effectively address the unique challenges posed by event data, highlighting the importance of tailored solutions for this task. To this end, we propose a novel method that constructs auxiliary modalities using frequency information from RGB and event tracklets, aligning them effectively through a fine-grained metric learning loss. Our approach not only achieves significant accuracy improvements over existing methods but also demonstrates the potential of event cameras for efficient and scalable edge AI surveillance applications. All source code and benchmarks are publicly available at https://github.com/yxgnahz/ASPDAC26-Event-RGBReID.