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AthletiQ:AI驱动的板球生物力学分析平台

AthletiQ是一个专业级的板球运动表现诊断平台,结合SAM2分割、MediaPipe姿态估计和生成式AI技术,将训练视频转化为可操作的技术反馈。

AIcomputer visionbiomechanicscricketSAM2MediaPipesports analyticsgenerative AIpose estimation
发布时间 2026/05/10 13:52最近活动 2026/05/10 13:59预计阅读 6 分钟
AthletiQ:AI驱动的板球生物力学分析平台
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章节 01

AthletiQ: AI-Driven Cricket Biomechanics Analysis Platform (Main Overview)

AthletiQ is a professional cricket performance diagnosis platform developed by milansinghal2004. It integrates cutting-edge AI and computer vision technologies (SAM2, MediaPipe, R3D-18 CNN, Ollama LLM) to convert training videos into actionable biomechanical feedback, aiming to provide elite-level insights for players and coaches.

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章节 02

Background & Project Purpose

AthletiQ was created to address gaps in traditional cricket motion analysis—such as insufficient player segmentation accuracy and lack of contextual, coach-like feedback. Its core goal is to offer scientific, data-driven technical guidance to help players refine their skills, from professionals to hobbyists.

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章节 03

Technical Methods & System Architecture

Core Components:

  1. Meta SAM2: High-precision object tracking for player segmentation in complex scenes.
  2. MediaPipe: 12-point key pose extraction (elbow, knee, hip, shoulder etc.) for fine-grained motion analysis.
  3. R3D-18 CNN: Classifies over 10 cricket shot types (Cover Drive, Pull, Flick etc.).
  4. Segmented DTW: Time alignment of player actions with professional benchmarks for fair comparison.
  5. Ollama LLM: Generates context-aware, coach-style technical reports.

System Architecture:

  • Frontend: Cyber-Command themed UI with glassmorphism (JS/CSS).
  • Orchestration: Node.js (Express) backend for session management and task triggering.
  • AI Processing: Python-based engine (Gradio for visualization).
  • Persistence: PostgreSQL (Neon Cloud) for data storage and history tracking.
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章节 04

Workflow: From Video Upload to Report Generation

  1. Authentication: User logs in to access upload interface.
  2. Video Upload: Sent to Node.js Express server.
  3. Analysis Trigger: Server starts Python subprocess for analysis.
  4. SAM2 Tracking: Object tracking and video clipping.
  5. Pose Extraction: MediaPipe extracts key pose data.
  6. Time Alignment: Segmented DTW aligns actions with benchmarks.
  7. Biomechanical Analysis: Calculate differences from ideal.
  8. Report Generation: Ollama LLM produces technical guidance.
  9. Result Display: Interactive dashboard shows insights.
  10. Data Archiving: Results stored in PostgreSQL for future reference.
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章节 05

Key Innovations & Technical Highlights

  1. Multimodal AI Fusion: Seamless integration of computer vision (SAM2), pose estimation (MediaPipe), time series analysis (DTW), and generative AI (Ollama) for end-to-end analysis.
  2. Interactive SVG Diagnostics: Real-time, clickable joint analysis with ideal range overlays for intuitive understanding.
  3. Scalable Distributed Architecture: Separated frontend, orchestration, and AI layers allow independent scaling for different usage scales.
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章节 06

Application Scenarios & Value

AthletiQ serves diverse users:

  • Professional Players: Quantify and refine technical details.
  • Youth Camps: Provide scientific action correction for young players.
  • Sports Researchers: Support biomechanics studies with data.
  • Hobbyists: Access professional-level analysis for skill improvement.
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章节 07

Conclusion & Significance

AthletiQ exemplifies deep AI application in sports science. By combining state-of-the-art computer vision and generative AI, it opens new possibilities for scientific cricket training. For SportsTech developers and researchers, it offers a valuable reference for building AI-powered biomechanics analysis tools.