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README.md

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+---
+license: mit
+tags: [tennis, ball-tracking, tracknet, heatmap, pytorch]
+---
+
+# TrackNet tennis ball tracker (mirror)
+
+> **Not trained by me.** These are the published weights from
+> [yastrebksv/TrackNet](https://github.com/yastrebksv/TrackNet), mirrored here so
+> a deployment can pull every weight my tennis pipeline needs from one place.
+> All credit for the model and its training belongs to the original author.
+
+## What it is
+
+`BallTrackerNet`, 10.7M parameters. A VGG-style encoder with a deconv decoder -
+a CNN, not a transformer. It takes **three consecutive frames** stacked into 9
+channels at 360x640 and outputs a per-pixel classification over 256 intensity
+levels; the ball is the argmax.
+
+Three frames is the whole point. A tennis ball in broadcast footage is ~10 px
+and motion-blurred into a streak, frequently indistinguishable from a line
+marking in any single frame. Temporal context is what a per-frame detector like
+YOLO structurally cannot use.
+
+## Getting a position out of the heatmap
+
+Taking the brightest pixel is fragile - one hot pixel on a shoe wins outright.
+Better: threshold the heatmap, run `cv2.HoughCircles`, and take the circle
+nearest the previous frame's ball. That uses blob shape *and* the fact that a
+ball cannot teleport.
+
+## Origin
+
+TrackNet: Huang, Liao, Chen, Ik, Peng (NCTU Taiwan),
+[arXiv:1907.03698](https://arxiv.org/abs/1907.03698), AVSS 2019. The original
+was Keras; yastrebksv's is the PyTorch reimplementation these weights come from.
+
+Trained on the TrackNet tennis dataset - 81 broadcast clips, 10 matches,
+19,835 labelled frames.