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🚨 Just built a system that reconstructs 4D NBA plays from a single broadcast video. 📦 Pipeline includes: • Object Detection • Camera Stabilization • Pose Tracking • Homography • Ball Path See more examples on my page—happy to answer any questions below.

10,378 次观看 • 1 年前 •via X (Twitter)

6 条评论

Daniel 的头像
Daniel1 年前

lit 🔥

Finn 的头像
Finn1 年前

I was playing around a bit with YOLO, how do you get from 2d poses to 3d, so lift them? Havent really figured that out. Any repo you use?

Effxcts 的头像
Effxcts1 年前

There are a bunch out there. Best one for video right now is probably GVHMR. There’s also Vid2Avatar and others that are coming out from CVPR 2025

Finn 的头像
Finn1 年前

and how do you deal with the relation of the players on the field? so their position. Been playing around with DPVO.

Effxcts 的头像
Effxcts1 年前

Camera stabilization and then homography. Couldn’t get DVPO installed on windows so did it that way

BIG P, THE TIGER 🐅 的头像
BIG P, THE TIGER 🐅1 年前

holy cow this is awesome

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AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

121,478 次观看 • 6 个月前