发表机构
Anhui University; State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology; Anhui Provincial Key Laboratory of Security Artificial Intelligence; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽大学; 光电子信息采集与保护技术国家重点实验室; 安徽省安全人工智能重点实验室; 安徽省多模态认知计算重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有基准无法有效评估动态RGBT跟踪器在真实场景下鲁棒性的问题，构建DRGBT-1K基准测试，提供全面注释，评估多种跟踪方法，发布未对齐版本及衍生数据集，并开发在线评估平台，为相关研究提供有力支持。
AI 中文摘要
动态RGBT跟踪旨在当可用传感模式和观测平台随时间变化时持续定位目标。与传统固定输入和观测平台的RGBT跟踪相比，它更符合现实世界协作感知系统。然而现有基准不足以系统评估真实动态模式变化和跨平台转换下跟踪器的鲁棒性。为此构建了DRGBT-1K基准测试，包含1045个真实场景序列和795K帧对，提供了包括密集边界框等全面注释，评估了20种多模态跟踪方法，发布未对齐版本并衍生出UGVT-1K，还开发了在线评估平台及排行榜。
英文摘要
Dynamic RGBT (DRGBT) tracking aims to continuously localize a target when the available sensing modalities and observation platforms vary over time. Compared with conventional RGBT tracking with fixed RGBT inputs and a fixed observation platform, DRGBT tracking is more consistent with real-world collaborative perception systems, where targets may be observed by heterogeneous sensors from different viewpoints. However, existing benchmarks are still insufficient for systematically evaluating tracker robustness under real dynamic modality variations and cross-platform transitions. To address this limitation, we make the following contributions. 1) We construct DRGBT-1K, a large-scale high-quality benchmark for DRGBT tracking. It contains 1,045 sequences captured entirely in real-world scenarios and 795K RGBT frame pairs collected using UAVs and handheld RGBT devices, encompassing diverse real-world scenes, pronounced viewpoint changes, modality variations, and target appearance discontinuities. 2) We provide comprehensive annotations for fine-grained evaluation, including dense bounding boxes, target category labels, challenge attributes, frame-level modality labels and platform labels. DRGBT-1K covers 24 target categories, more than 15 scene types and 15 challenge attributes. 3) We establish a comprehensive benchmark by evaluating 20 representative multimodal tracking methods, including conventional RGBT trackers, modality-missing RGBT trackers, and DRGBT trackers under a unified evaluation protocol. 4) We release an unaligned version of DRGBT-1K and derive UGVT-1K to support broader research on unaligned multimodal tracking and UAV-ground collaborative tracking. 5) We develop an online evaluation platform for DRGBT-1K and provide a leaderboard that collects all methods evaluated on this benchmark.
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