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This paper introduces computational neuromorphic tracking (CNT), a physics-informed framework that leverages asynchronous event sensing and task-driven speckle analysis to track fast-moving objects through strongly scattering media in low-light. CNT formulates neuromorphic speckle aggregation as a spatiotemporal representation, jointly optimizing temporal and spatial parameters for tracking stability. Experiments show CNT enables robust motion tracking at 10x faster speeds and 10x dimmer illumination compared to conventional frame-based systems.
See around corners and through murky water: neuromorphic event cameras plus physics-informed speckle analysis now track objects moving 10x faster in conditions 10x darker than previously possible.
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.