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

• URL: http://arxiv.org/abs/2604.25310v1
• Date: Tue, 28 Apr 2026 07:19:52 GMT
• Title: Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis
• Abstract summary: This work addresses the problem of tracking fast-moving objects through strongly scattering media in a low-light environment.<n>We introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines event sensing with task-driven speckle analysis for robust motion estimation.
• Score: 14.631158841982058
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Generative Motion In-betweening by Diffusion over Continuous Implicit Representations [8.190723030003804]
  We propose a novel pipeline and sampling optimization strategy for latent diffusion models (LDM) based on motion implicit neural representations (INR)<n>Our model can sample the INR parameters from extremely sparse and ambiguous data and reconstruct plausible and smooth motions from the manifold.
  arXiv  Detail & Related papers  (2026-05-12T21:48:14Z)
• Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency [74.90075313101933]
  This paper revisits the same optical-flow primitive through a more local supervision design.<n>We use adjacent-frame Eulerian motion fields to guide generation, where the motion signal always describes a short temporal hop.<n>To mitigate the drift artifacts common in adjacent frame generation, we introduce a Bidirectional Geometric Consistency mechanism.
  arXiv  Detail & Related papers  (2026-05-07T13:53:31Z)
• Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
  Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
  arXiv  Detail & Related papers  (2026-04-27T04:36:41Z)
• Hypergraph-State Collaborative Reasoning for Multi-Object Tracking [63.32950991964095]
  We propose a collaborative reasoning framework that enhances motion estimation through joint inference among multiple correlated objects.<n>By allowing objects with similar motion states to mutually constrain and refine each other, our framework stabilizes noisy trajectories and infers plausible motion continuity even when target is occluded.
  arXiv  Detail & Related papers  (2026-04-14T12:37:39Z)
• Event-based Visual Deformation Measurement [76.25283405575108]
  Visual Deformation Measurement aims to recover dense deformation fields by tracking surface motion from camera observations.<n>Traditional image-based methods rely on minimal inter-frame motion to constrain the correspondence search space.<n>We propose an event-frame fusion framework that exploits events for temporally dense motion cues and frames for spatially dense precise estimation.
  arXiv  Detail & Related papers  (2026-02-16T01:04:48Z)
• HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking [80.07224739976911]
  Event cameras offer exceptional temporal resolution and a range (modal)<n> RGB cameras excel at capturing rich texture with high resolution, whereas event cameras offer exceptional temporal resolution and a range (modal)
  arXiv  Detail & Related papers  (2025-10-22T13:15:13Z)
• Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing [2.5465367830324905]
  Event-based eye tracking holds significant promise for fine-grained cognitive state inference.<n>We introduce a model-agnostic, inference-time refinement framework to enhance the output of existing event-based gaze estimation models.
  arXiv  Detail & Related papers  (2025-06-14T14:48:11Z)
• EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
  Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
  arXiv  Detail & Related papers  (2025-03-14T13:15:54Z)
• MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>To resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process.<n>The method improves the $Survival_50$ metric by 17.9% over event-only tracking of any point baseline.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimation [20.002238735553792]
  We propose a novel solution to tight visual-inertial fusion directly at the level of first-order kinematics by employing a dynamic vision sensor instead of a normal camera. We demonstrate how velocity estimates in highly dynamic situations can be obtained over short time intervals. Experiments on both simulated and real data demonstrate that the proposed tight event-inertial fusion leads to continuous and reliable velocity estimation.
  arXiv  Detail & Related papers  (2024-01-17T15:56:57Z)
• Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling [7.111443975103329]
  In this paper, we explore the optical flow estimation from multiple-frame sequences of dynamic scenes. We use motion priors of the adjacent frames to provide more reliable supervision of the occluded regions. Experiments on KITTI 2012, KITTI 2015, Sintel Clean, and Sintel Final datasets demonstrate the effectiveness of our methods.
  arXiv  Detail & Related papers  (2023-04-14T14:32:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.