Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

2026-04-28

AI Summary

This work addresses the challenge of robustly tracking high-speed targets under extreme conditions of strong scattering and ultra-low illumination, where conventional frame-based cameras fail to maintain stable performance. The authors propose a computational neuromorphic tracking framework that, for the first time, integrates physical priors into neuromorphic vision. By synergistically combining asynchronous event cameras with task-driven speckle analysis, the method constructs a spatiotemporal speckle representation and jointly optimizes its spatial and temporal parameters. This approach overcomes the traditional trade-off between signal-to-noise ratio and temporal resolution, enabling reliable tracking in scenarios ten times darker and with target velocities ten times higher than those manageable by conventional systems, thereby significantly expanding the operational boundaries of high-speed, low-light vision through scattering media.

Abstract

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.

Problem

• scattering media
• low-light tracking
• fast-moving objects
• motion estimation
• speckle analysis

Innovation

• neuromorphic sensing
• speckle analysis
• scattering media
• event-based vision
• physics-informed learning