You can deploy this model and start earning money today!

vishnushenoy09/
tracknet-v1-tennis

Name: tracknet-v1-tennis
Availability: InStock

Views

No views yet

TrackNet V1 — Tennis Ball Tracking

TrackNet V1 weights for high-speed tennis ball detection from broadcast video. The model takes 3 consecutive frames (resized to 640x360) and outputs a probability heatmap of the ball position.

Architecture

Input: 3 consecutive RGB frames (640x360) = 9-channel input
Output: 640x360 heatmap (ball probability)
Backbone: Custom CNN with encoder-decoder architecture (DeconvNet-style)

Usage

python

import torch
from tracknet import BallDetectorV2

detector = BallDetectorV2(path_model="tracknet_weights.pth", device="mps")
ball_positions = detector.infer_model(frames, use_frame_diff=True)

Credits

TrackNet by Yu-Chuan Huang, I-No Liao, Ching-Hsuan Chen, Tsui-Kang Ik, Wen-Chih Peng. Paper: TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications (AVSS 2019).
Implementation adapted from yastrebksv/TrackNet and yastrebksv/TennisProject. Enhanced with frame-differencing and Kalman smoothing for this project.

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/tracknet-v1-tennis