FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision

• URL: http://arxiv.org/abs/2603.19770v1
• Date: Fri, 20 Mar 2026 08:59:37 GMT
• Title: FlashCap: Millisecond-Accurate Human Motion Capture via Flashing LEDs and Event-Based Vision
• Abstract summary: A millisecond difference may determine victory or defeat in sports competitions.<n>PMT is achieved using high-speed RGB cameras in specialized scenarios such as the Olympic Games.<n>We develop FlashCap, the first flashing LED-based MoCap system for PMT.
• Score: 70.26305628326237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise motion timing (PMT) is crucial for swift motion analysis. A millisecond difference may determine victory or defeat in sports competitions. Despite substantial progress in human pose estimation (HPE), PMT remains largely overlooked by the HPE community due to the limited availability of high-temporal-resolution labeled datasets. Today, PMT is achieved using high-speed RGB cameras in specialized scenarios such as the Olympic Games; however, their high costs, light sensitivity, bandwidth, and computational complexity limit their feasibility for daily use. We developed FlashCap, the first flashing LED-based MoCap system for PMT. With FlashCap, we collect a millisecond-resolution human motion dataset, FlashMotion, comprising the event, RGB, LiDAR, and IMU modalities, and demonstrate its high quality through rigorous validation. To evaluate the merits of FlashMotion, we perform two tasks: precise motion timing and high-temporal-resolution HPE. For these tasks, we propose ResPose, a simple yet effective baseline that learns residual poses based on events and RGBs. Experimental results show that ResPose reduces pose estimation errors by ~40% and achieves millisecond-level timing accuracy, enabling new research opportunities. The dataset and code will be shared with the community.

Related papers

• A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras [51.7262911415172]
  We develop an event-based active marker system to collect real-world data with ground truth from a motion capture system.<n>Experiments on both synthetic and real data demonstrate improved accuracy over the state-of-the-art P2P solver.
  arXiv  Detail & Related papers  (2026-08-10T12:19:24Z)
• Event-based Batting Impact Estimation [1.6111818380407035]
  We propose a novel framework to estimate impact timing based on the weighted centroid distance between the detected ball and bat.<n> Experiments on real-world datasets demonstrate that our method achieves superior accuracy under challenging conditions.
  arXiv  Detail & Related papers  (2026-05-25T10:04:14Z)
• DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions [31.62492338790292]
  Human Action Recognition (HAR) is a fundamental computer vision task with diverse real-world applications.<n>Current research faces two key challenges: absence of a benchmark integrating low-light conditions, 6-DoF motion, and synchronized IMU data.<n>We propose Event-IMU Stabilized HAR (EIS-HAR), with two modules.
  arXiv  Detail & Related papers  (2026-05-20T03:47:47Z)
• 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)
• Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis [0.0]
  We introduce a novel, preliminary multi-resolution and multi-modal micro-expression dataset recorded with synchronized RGB and event cameras.<n>We show that event-based data can be used for micro-expression recognition and frame reconstruction.
  arXiv  Detail & Related papers  (2025-08-16T09:03:20Z)
• Event-Driven Dynamic Scene Depth Completion [50.01494043834177]
  EventDC is the first event-driven depth completion framework.<n>It consists of two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF)
  arXiv  Detail & Related papers  (2025-05-19T16:02:37Z)
• Quantitative evaluation of brain-inspired vision sensors in high-speed robotic perception [4.9554369813645325]
  Perception systems in robotics encounter significant challenges in high-speed and dynamic conditions when relying on traditional cameras.<n>Brain-inspired vision sensors (BVS) have recently gained attention as an alternative, offering high temporal resolution with reduced bandwidth and power.<n>Here, we present the first quantitative evaluation framework for two representative classes of BVSs in variable-speed robotic sensing.
  arXiv  Detail & Related papers  (2025-04-27T14:24:06Z)
• 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)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks [107.62975594230687]
  We demonstrate an application where event cameras excel: accurately estimating the impact location of fast-moving objects. We introduce a lightweight event representation called Binary Event History Image (BEHI) to encode event data at low latency. We show that the system is capable of achieving a success rate of 81% in catching balls targeted at different locations, with a velocity of up to 13 m/s even on compute-constrained embedded platforms.
  arXiv  Detail & Related papers  (2023-04-14T15:23:28Z)
• E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition [21.199869051111367]
  Event cameras capture pixel-level intensity changes in the form of "events" N-EPIC-Kitchens is the first event-based camera extension of the large-scale EPIC-Kitchens dataset. We show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time.
  arXiv  Detail & Related papers  (2021-12-07T09:43:08Z)

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.