用视频数据还原《Dota 2》比赛中的视野信息，补足传统数据的盲区。
Computer Vision for MOBA Analytics: A Dataset and Baseline for Visibility Analysis in Dota 2
- 基于双视角视频与手动标注小地图，构建了144场国际邀请赛的可见性分析数据集
- YOLO11l模型在密集场景下准确识别玩家图标，生成高精度可见性时间曲线
- 揭示英雄、角色和团队层级的视觉行为模式，适合游戏分析与智能系统研究
多数多人在线战术竞技场（MOBA）分析依赖结构化数据，无法直接反映比赛中各队伍实际可见内容。本文提出Dota2-Vis，一个基于视频的数据集及可见性分析基线流程，涵盖2025年国际邀请赛全部144场比赛，从双方视角录制，共288段全高清视频，并包含2,477张手动标注的小地图图像。我们评估多种现代目标检测器在识别玩家图标上的表现，选用最优模型（YOLO11l）估算对手可见的玩家存在情况。结果表明，该模型在密集且视觉复杂的小地图场景中仍能可靠识别玩家图标。生成的可见性曲线揭示了玩家、英雄、角色与团队层级的行为模式，补充了传统数据分析难以获取的洞察。数据集与代码已开源。
原文摘要 · Abstract (English)
Introduction: Most Multiplayer Online Battle Arena (MOBA) analytics studies rely on structured data, which does not directly capture what each team could actually see during a match. Objective: This work introduces Dota2-Vis, a video-based dataset, and a baseline pipeline for visibility analysis in professional Dota 2 matches. Methodology: The dataset comprises all 144 matches from The International 2025, recorded from both team perspectives, totaling 288 Full HD videos, together with 2,477 manually annotated minimap images. We evaluate multiple variants of a modern object detector for player-icon detection and use the best-performing model to estimate opponent-visible player presence over time. Results: YOLO11l (large) achieved the best overall performance, reliably identifying player icons even in dense and visually cluttered minimap scenes. The resulting visibility curves reveal player, hero, role, and team-level patterns that complement conventional MOBA analytics, highlighting behavioral differences that are difficult to obtain from structured data alone. The dataset and code are publicly available at https://github.com/RicardoRCarvalho/dota2-vis/.
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