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i trained a computer vision model for beach volleyball analysis — using roboflow rapid + SAM3 + python for detection of players, ball, net — automated stat collection and dataviz for ball touches, speed, height, net crosses could expand this prototype for individual player stats, court coverage heatmap, automated...

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739,377 görüntüleme • 4 ay önce •via X (Twitter)

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i trained a computer vision model to count chinups roboflow rapid + SAM3 detected the chinup bar - hands - head through natural language prompts then I wrote a python script to count completed reps (head above bar) and annotate the video idea for a gamified fitness app? automated tracking of your workout, with rep counts per exercise added to your database. this gives progress stats over time, and a leaderboard with your friends these new vision models are neat because they can detect exercise equipment, allowing tracking of dumbbell workouts, bench press, squat, etc.
0:30

i trained a computer vision model to count chinups roboflow rapid + SAM3 detected the chinup bar - hands - head through natural language prompts then I wrote a python script to count completed reps (head above bar) and annotate the video idea for a gamified fitness app? automated tracking of your workout, with rep counts per exercise added to your database. this gives progress stats over time, and a leaderboard with your friends these new vision models are neat because they can detect exercise equipment, allowing tracking of dumbbell workouts, bench press, squat, etc.

AA

1,180,068 görüntüleme • 6 ay önce

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.
4:41:24

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

119,813 görüntüleme • 1 ay önce

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.
4:28:35

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

zostaff

1,070,963 görüntüleme • 2 ay önce

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.
5:58:26

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.

zostaff

229,505 görüntüleme • 1 ay önce

Seen a post today… Stats lie. Show me the film! No stat for how deep you shoot that 3 or level of difficulty. No stat for the IQ it took to let go of that ball on the trap. No stat for turning around and immediately knowing it’s time for an inbound play. No stat for communication. No stat for scoring on 2 or 3 D1 level talent players, the points are same BUT different. No stat line for a player scoring on a triple team while the stands are chanting over rated. No stat for that timely screen or that hockey pass. No stat for motor bc you played all game and still drained the deep 3…. Recruits: Stats don’t tell the whole story! They tell a piece of that story. Stay the course!
0:22

Seen a post today… Stats lie. Show me the film! No stat for how deep you shoot that 3 or level of difficulty. No stat for the IQ it took to let go of that ball on the trap. No stat for turning around and immediately knowing it’s time for an inbound play. No stat for communication. No stat for scoring on 2 or 3 D1 level talent players, the points are same BUT different. No stat line for a player scoring on a triple team while the stands are chanting over rated. No stat for that timely screen or that hockey pass. No stat for motor bc you played all game and still drained the deep 3…. Recruits: Stats don’t tell the whole story! They tell a piece of that story. Stay the course!

Amber Mathurin ™️

12,649 görüntüleme • 2 yıl önce

1/ 🤖 New paper hot off the press 🐜 Automated behavioural analysis using machine learning is great, but still needs lots of effort to curate training data. What if a computer could generate that data for you? Say hello to replicAnt #OpenSource
0:47

1/ 🤖 New paper hot off the press 🐜 Automated behavioural analysis using machine learning is great, but still needs lots of effort to curate training data. What if a computer could generate that data for you? Say hello to replicAnt #OpenSource

Evolutionary Biomechanics Laboratory

54,805 görüntüleme • 2 yıl önce

- if SAM3 is so good, do we still need other models? - yes. SAM3 is too large for low compute edge devices and too slow for real-time use. we still need light and fast models for edge workloads. distill SAM3 into smaller model and deploy it in seconds with Roboflow rapid
0:36

- if SAM3 is so good, do we still need other models? - yes. SAM3 is too large for low compute edge devices and too slow for real-time use. we still need light and fast models for edge workloads. distill SAM3 into smaller model and deploy it in seconds with Roboflow rapid

SkalskiP

93,025 görüntüleme • 6 ay önce

This is a great short squeeze pattern for the current market. This is a quick overview of it, but if you want a full video with all the details for FREE just DM me and i'll send it over 👍
0:44

This is a great short squeeze pattern for the current market. This is a quick overview of it, but if you want a full video with all the details for FREE just DM me and i'll send it over 👍

Chris Lanzilotti

32,645 görüntüleme • 1 yıl önce

A talented artist off the court and a standout setter for Illinois Volleyball, Kenna Phelan channels her creativity in her art and playmaking for the Fighting Illini 🎨 At The Net x Illinois Volleyball
4:22

A talented artist off the court and a standout setter for Illinois Volleyball, Kenna Phelan channels her creativity in her art and playmaking for the Fighting Illini 🎨 At The Net x Illinois Volleyball

Big Ten Volleyball

13,415 görüntüleme • 8 ay önce

If you’re afraid of A.I. then I suggest closing your eyes. See ball. Hit ball. Process completely automated. They’re coming for our jobs!
0:37

If you’re afraid of A.I. then I suggest closing your eyes. See ball. Hit ball. Process completely automated. They’re coming for our jobs!

Patrick McDonald

98,135 görüntüleme • 2 yıl önce

If you are planning to attend the court for news coverage, please send me a direct message for details about the location and time😊
0:51

If you are planning to attend the court for news coverage, please send me a direct message for details about the location and time😊

Niyak Ghorbani (نیاک)

13,885 görüntüleme • 1 yıl önce

🗣️“If you want Test cricket to survive, you need a two-tier system.” 🏏 Do you agree with Ravi Shastri’s vision for the future of the red-ball game?
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🗣️“If you want Test cricket to survive, you need a two-tier system.” 🏏 Do you agree with Ravi Shastri’s vision for the future of the red-ball game?

Wisden

79,926 görüntüleme • 11 ay önce

AI is transforming every aspect of our lives, including how we experience sports. Listen to a fully #AI-generated recap of Wisła Kraków’s home match, created using Google #NoteBookML. The data for this summary was collected through a computer vision-based system that automatically tracks players’ skeletal movements with ultra-precision by ReSpo.Vision . This is the future of sports #analysis in action. Wisła Kraków 🔵⚪️🔴
17:30

AI is transforming every aspect of our lives, including how we experience sports. Listen to a fully #AI-generated recap of Wisła Kraków’s home match, created using Google #NoteBookML. The data for this summary was collected through a computer vision-based system that automatically tracks players’ skeletal movements with ultra-precision by ReSpo.Vision . This is the future of sports #analysis in action. Wisła Kraków 🔵⚪️🔴

Jarosław Królewski

21,828 görüntüleme • 1 yıl önce

Beach volleyball players really fight gravity for a living
0:24

Beach volleyball players really fight gravity for a living

Adorable Girls

23,099 görüntüleme • 1 ay önce

Announcing the official launch of BreakAway Speed - a fully automated, iPhone-based markerless sprinting solution for athletes & coaches. Take videos of a flying 10 or accel. and receive biomechanical feedback in <5 minutes! See video below for more information and reach out!
1:05

Announcing the official launch of BreakAway Speed - a fully automated, iPhone-based markerless sprinting solution for athletes & coaches. Take videos of a flying 10 or accel. and receive biomechanical feedback in <5 minutes! See video below for more information and reach out!

BreakAway Data

24,128 görüntüleme • 2 yıl önce

Chase Richardson offers everything you want in a Bucky Ball guard. Speed, aggression, vision, shooting, creativity, and downhill driving. He’s the perfect fit for this system, which is why he’s been a major priority for this staff‼️ Chase Richardson
2:06

Chase Richardson offers everything you want in a Bucky Ball guard. Speed, aggression, vision, shooting, creativity, and downhill driving. He’s the perfect fit for this system, which is why he’s been a major priority for this staff‼️ Chase Richardson

Luke Evangelist

13,008 görüntüleme • 1 ay önce

I got dirtman (b/sac) vision model working on the desk robot. I’m excited for all of the new use cases that computer vision will unlock for automated welding.
0:35

I got dirtman (b/sac) vision model working on the desk robot. I’m excited for all of the new use cases that computer vision will unlock for automated welding.

Julian Fried

51,246 görüntüleme • 1 yıl önce