Project Status: GameVision Benchmarking Engine

Date: January 11, 2026
Status: Initial R&D Phase

Overview

GameVision is an experimental real-time analytics engine designed to benchmark multimodal data synchronization in professional esports. The project focuses on the temporal gap between official game state events and their representation in public broadcast streams.

Core Objectives

We are building a system that correlates server-side game data (the “ground truth”) with low-latency computer vision analysis of live video feeds. This research aims to:

  • Validate Vision Models: Train and evaluate the accuracy of Vision-Language Models (VLMs) and OCR systems in high-intensity gaming environments.
  • Temporal Latency Mapping: Quantify the delay introduced by broadcast encoding, transcoding, and distribution networks.
  • Predictive Win-Probability: Generate synchronized live-score overlays and real-time win-probability insights that account for broadcast lag.

Technical Approach

The engine utilizes a dual-ingestion pipeline:

  1. Direct Data Feed: Integration with official game server APIs to capture events in millisecond resolution.
  2. Visual Stream Analysis: Real-time processing of 60fps video streams using custom computer vision models to identify in-game UI changes.

By minimizing the information gap between the game server and the broadcast experience, GameVision aims to provide a more responsive and accurate “second-screen” experience for esports fans and researchers.

Privacy & Ethics

GameVision is designed for research and data analysis purposes. We prioritize the integrity of the esports ecosystem and adhere to all publisher data usage policies. The project does not collect personal user data.

Contact

For inquiries regarding the technical implementation or data findings, please contact: Email: likaiwei99@gmail.com —