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

TL;DR

This paper introduces a physics-informed neuromorphic speckle analysis framework for robustly tracking fast-moving objects through scattering media in low-light environments, outperforming traditional methods.

Contribution

It presents a novel computational neuromorphic tracking approach combining asynchronous event sensing with speckle analysis, enabling high-speed and low-light tracking.

Findings

Robust motion tracking of 10x faster objects achieved.

Operates under 10x dimmer illumination than conventional systems.

Significantly broadens tracking capabilities in challenging conditions.

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.