Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding

• URL: http://arxiv.org/abs/2607.11844v1
• Date: Mon, 13 Jul 2026 17:34:36 GMT
• Title: Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding
• Abstract summary: We introduce SportMV-Bench, a benchmark for multi-view sports video understanding.<n>Our analysis shows that current MLLMs fail to effectively exploit multi-view information.<n>We propose SportMV-Agent, an agentic framework that orchestrates an iterative loop of active view selection, perception tool execution, and evidence-grounded reasoning.
• Score: 21.581174236498196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos involve dense occlusion, rapid motion, and complex interactions that are difficult to resolve from a single viewpoint. In practice, sports events are recorded from multiple camera angles, providing complementary evidence used by referees. Yet, no existing benchmark evaluates MLLMs on multi-view sports video understanding. To address this gap, we introduce SportMV-Bench, a comprehensive benchmark built from official match recordings, through a dedicated pipeline combining LLM-based generation, MLLM-based verification, and human filtering to ensure quality and consistency. SportMV-Bench containing 787 multi-view video bundles and 2592 question-answer pairs across three categories: Perception-Aware Recognition (PAR), Rule-aware Event Interpretation (REI), and Adjudicative Decision Reasoning(ADR). Our analysis shows that current MLLMs fail to effectively exploit multi-view information, with the bottlenecks lying in fine-grained visual perception and view selection rather than logical reasoning or domain knowledge. We propose SportMV-Agent, an agentic framework that orchestrates an iterative loop of active view selection, perception tool execution, and evidence-grounded reasoning, achieving a significant 14.46% relative improvement over the strongest MLLM baseline.

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