Prompt, Probe, Train, or Annotate? Single-camera sports video understanding in amateur settings

arXiv:2609.28049 · cs.CV, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Accuracy — 96× in this paper · explained in 1 episode
• Benchmark — 46× in this paper · explained in 2 episodes
• Actor — 22× in this paper · explained in 3 episodes
• Abstention — 7× in this paper · explained in 1 episode
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Agentic Reasoning — 4× in this paper · explained in 1 episode
• Grounding — 4× in this paper · explained in 3 episodes
• Redundancy — 4× in this paper · explained in 2 episodes
• Temporal Reasoning — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Calibration — 3× in this paper · explained in 7 episodes
• Commentary — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Abstention mechanism — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agentic System — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
• MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
• A General Framework for Jersey Number Recognition in Sports Video
• Towards long-term player tracking with graph hierarchies and domain-specific features
• SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
• SoccerNet 2025 Challenges Results
• SoccerNet 2026 Challenges Results
• Revisiting Feature Prediction for Learning Visual Representations from Video
• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
• Temporal vs. Spatial: Comparing DINOv3 and V-JEPA2 Feature Representations for Video Action Analysis
• VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification
• SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports
• RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees
• What Does Vision Tool-Use Reinforcement Learning Really Learn? Disentangling Tool-Induced and Intrinsic Effects for Crop-and-Zoom

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