用事件数据的几何方向性修复模糊图像，提升高速运动目标追踪精度
Spatial Orthogonal Refinement for Robust RGB-Event Visual Object Tracking
- 通过方向正交滤波从事件流中提取清晰结构响应
- 在模糊和低光条件下，跟踪精度超越现有融合方法
- 适合需要高动态场景下精准追踪的研究与工程应用
高动态运动场景中，传统RGB传感器易因运动模糊导致追踪性能下降。事件相机具备微秒级时间分辨率和高动态范围，可提供互补的结构信息。然而，现有RGB-事件融合方法通常将事件数据视为密集强度图，采用黑箱融合策略，未能显式利用事件流中固有的方向几何先验来修正退化的RGB特征。为此，本文提出SOR-Track，一种基于空间正交精炼（Spatial Orthogonal Refinement, SOR）的轻量级多模态追踪框架。核心SOR模块采用一组由局部运动方向动态引导的正交方向滤波器，从事件流中提取锐利且运动一致的结构响应。这些响应作为几何锚点，通过非对称结构调制机制，对畸变的RGB纹理进行调制与精炼，从而显式弥合两模态间的结构差异。在大规模FE108基准上的大量实验表明，SOR-Track在运动模糊和低光照条件下均持续优于现有融合追踪器。尽管结构简单，该方法提供了一种原理清晰、物理可信的多模态特征对齐与纹理修复方案。代码将发布于https://github.com/Event-AHU/OpenEvTracking。
原文摘要 · Abstract (English)
Robust visual object tracking (VOT) remains challenging in high-speed motion scenarios, where conventional RGB sensors suffer from severe motion blur and performance degradation. Event cameras, with microsecond temporal resolution and high dynamic range, provide complementary structural cues that can potentially compensate for these limitations. However, existing RGB-Event fusion methods typically treat event data as dense intensity representations and adopt black-box fusion strategies, failing to explicitly leverage the directional geometric priors inherently encoded in event streams to rectify degraded RGB features. To address this limitation, we propose SOR-Track, a streamlined framework for robust RGB-Event tracking based on Spatial Orthogonal Refinement (SOR). The core SOR module employs a set of orthogonal directional filters that are dynamically guided by local motion orientations to extract sharp and motion-consistent structural responses from event streams. These responses serve as geometric anchors to modulate and refine aliased RGB textures through an asymmetric structural modulation mechanism, thereby explicitly bridging structural discrepancies between two modalities. Extensive experiments on the large-scale FE108 benchmark demonstrate that SOR-Track consistently outperforms existing fusion-based trackers, particularly under motion blur and low-light conditions. Despite its simplicity, the proposed method offers a principled and physics-grounded approach to multi-modal feature alignment and texture rectification. The source code of this paper will be released on https://github.com/Event-AHU/OpenEvTracking
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