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vishnushenoy09/
catboost-tennis-bounce

Name: catboost-tennis-bounce
Availability: InStock

tabular-classification

1706.09516

mit

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CatBoost Tennis Bounce Detector

A CatBoost gradient boosting model trained to detect ball bounces from kinematic features derived from ball tracking data.

Input Features

The model takes kinematic features computed from ball trajectory data, including:

Ball position (x, y) over a sliding window
Velocity and acceleration components
Vertical direction changes (V-shape patterns)
Frame-to-frame displacement

Usage

python

from catboost import CatBoostClassifier

model = CatBoostClassifier()
model.load_model("bounce_model.cbm")
predictions = model.predict(features)

Post-processing

Predictions are validated with:

V-shaped Y-reversal confirmation
Court-view status filtering
Minimum inter-bounce gap enforcement

Credits

CatBoost by Yandex. Paper: CatBoost: unbiased boosting with categorical features (NeurIPS 2018). Developed by Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin.
Bounce detection approach inspired by yastrebksv/TennisProject. Training data derived from ball trajectories extracted via TrackNet from TenniSet broadcast videos.

Part of

This model is part of the Sports Commentator from Video project — an automated tennis commentary pipeline using TrackNet, CatBoost, VideoMAE, Qwen2.5, RF-DETR, and F5-TTS.

vishnushenoy09/catboost-tennis-bounce