🏏 Cricket Shot Quality Analyser

BTP Project — "Quantifying the Quality/Correctness of a Cricket Shot Using AI" Timeline: 3 months | Built on RITIK-12/CricketShotClassification (MIT License)

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Current State (Week 1)

Working Streamlit app that:

• Classifies a cricket shot from a video (10 shot types, 94% accuracy)
• Compares two videos using cosine similarity of EfficientNet features

Setup

git clone https://github.com/vipulpandey21/CricketShotQualityAI.git
cd CricketShotQualityAI

py -3.11 -m venv venv
venv\Scripts\activate

pip install -r requirements.txt
streamlit run app.py

Project Structure

app.py                  ← Streamlit UI
model_weights.h5        ← Pre-trained EfficientNetB0+GRU weights (from Ritik's work)
src/
  utils/
    video_utils.py      ← Frame extraction from video
Notebooks/              ← Original training notebooks (reference only)

Shot Classes

cover, defense, flick, hook, late_cut, lofted, pull, square_cut, straight, sweep

Roadmap

• Week 1 — Shot classification + similarity working app
• Week 2-3 — Add MediaPipe pose estimation, extract joint keypoints
• Week 4-5 — Build biomechanical scoring rules per shot type
• Week 6-8 — Quality score (0-100) + coaching feedback UI
• Week 9-12 — Regression model, reference shot library, final report

Credits

• Base model: RITIK-12/CricketShotClassification — MIT License
• Dataset: CricShotClassify / CrickShot10 — Sen et al., Sensors 2021