eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers

2026-01-10

arXiv.org

AI Summary

This work addresses the challenge of robustly tracking high-speed skiers in broadcast videos characterized by motion blur, static overlays, and visual clutter—conditions under which conventional RGB-based methods struggle. To enable a fair comparison between RGB and event-based modalities without relying on neural interpolation, the authors introduce eSkiTB, the first synthetic event-camera tracking dataset tailored for winter sports, generated via direct video-to-event conversion. Evaluations using the spike-transformer-based SDTrack and the RGB baseline STARK demonstrate that, in scenes dominated by static overlays, the event modality achieves an IoU of 0.685, outperforming RGB methods by 20.0 percentage points. The overall average IoU of 0.711 confirms the robustness and effectiveness of event cameras for tracking ballistic-motion targets in visually crowded environments.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

Tracking skiers in RGB broadcast footage is challenging due to motion blur, static overlays, and clutter that obscure the fast-moving athlete. Event cameras, with their asynchronous contrast sensing, offer natural robustness to such artifacts, yet a controlled benchmark for winter-sport tracking has been missing. We introduce event SkiTB (eSkiTB), a synthetic event-based ski tracking dataset generated from SkiTB using direct video-to-event conversion without neural interpolation, enabling an iso-informational comparison between RGB and event modalities. Benchmarking SDTrack (spiking transformer) against STARK (RGB transformer), we find that event-based tracking is substantially resilient to broadcast clutter in scenes dominated by static overlays, achieving 0.685 IoU, outperforming RGB by +20.0 points. Across the dataset, SDTrack attains a mean IoU of 0.711, demonstrating that temporal contrast is a reliable cue for tracking ballistic motion in visually congested environments. eSkiTB establishes the first controlled setting for event-based tracking in winter sports and highlights the promise of event cameras for ski tracking. The dataset and code will be released at https://github.com/eventbasedvision/eSkiTB.

Problem

• ski tracking
• motion blur
• static overlays
• visual clutter
• event-based vision

Innovation

• event camera
• synthetic dataset
• ski tracking
• spiking transformer
• video-to-event conversion