用事件相机+物理模型实现强散射下的高速低光运动追踪
Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis
- 结合事件相机与任务驱动的斑点分析，实现异步感知
- 在10倍更暗光照下追踪速度提升10倍，稳定性显著增强
- 适合高速动态、极低光照场景，如生物成像或深海探测
本工作解决在低光环境下通过强散射介质追踪快速运动物体的关键问题。不同于使用固定曝光时间帧式相机的方法（牺牲信噪比以换取时间分辨率），本文提出计算神经形态追踪（CNT）框架，融合异步事件传感与任务驱动的斑点分析，实现鲁棒运动估计。将神经形态斑点聚合建模为时空斑点表示，联合优化时空参数以在极端条件下最大化追踪稳定性。大量实验表明，该方法可在10倍更暗光照下实现10倍更快的运动追踪，显著扩展了散射介质中追踪的适用范围，为高动态与低光照场景提供了高效可扩展的解决方案。
原文摘要 · Abstract (English)
This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.
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