Advanced Computer Vision Techniques

Rarely do I miss an opportunity to apply techniques from work to things I enjoy doing in my free time. Often that thing is cycling, the greatest sport in the world. But when autumn comes around and the streets turn cold and dark, I prefer the iron of the weight room over the carbon of the road. A technical sport like Olympic weightlifting is of course best mastered with the guidance of an experienced coach. To the less fortunate or ambitious, video analysis may help hone skills and track progress. Various apps offer basic video tools, but I’ve yet to find one that provides the data I am truly interested in: ground reaction forces (GRFs), forces applied to the barbell, accurate kinematics, and mechanical power output. That is why I developed my own weightlifting video analyzer. The demo below is the result of several techniques from work combined: AI & computer vision (Raidyn), state-input estimation & multibody dynamics (KU Leuven Mecha(tro)nic System Dynamics (LMSD)), and biomechanical impact modelling (Classified Cycling). At the core is a flexible multibody model of the barbell that feeds into a combined state-input-parameter estimator to infer the forces applied to the bar. With accurate barbell forces, GRFs can be estimated  with higher precision than methods based solely on accelerations derived from video. In addition, the model unlocks a virtually unlimited supply of synthetic training data for the AI model, ensuring robust segmentation of barbell motion and deformation. Initially I cast myself as the hero of the demo, but I soon realized that a video starring the Pogačar of weightlifting would make for a more spectacular analysis. Enter Bulgarian Karlos Nasar, 20 years of age at this year’s European Championships in Moldova, lifting a record-breaking 229 kg overhead. Running my analyzer on Nasar’s low-resolution YouTube video allowed me to test its performance on a subject it hadn’t seen before in training, with frame rate and resolution far below what I use in my own sessions – and yet the algorithms didn’t miss a beat. Sure, it helps that weightlifting consists of only two precisely defined movement patterns, but I was still pleasantly surprised. The demo below is just the start. Belgium’s cold season still has a way to go and I’ve got plenty of ideas to improve and expand the algorithms. Next up: joint and muscle force estimation. 

Robots on the pitch....You better believe it. Will you be able to play with this one? No more standing cones or passive drills. Athletes today are dodging dynamic robots—machines that track, move, and react in real time. These aren’t gimmicks; they’re next-gen training partners. ⚽ In football, systems like SKILLSLAB, Rezzil, and Trailblazer Training Bots are already used by top clubs to simulate high-pressure situations, improve decision-making, and measure milliseconds of reaction time. 🏀 In basketball, robotic arms help perfect shooting arcs, while AI vision tools break down footwork frame by frame. 🎾 In tennis, smart ball machines adjust spin, speed, and placement in unpredictable sequences—training the brain as much as the body. Why it matters: + Athletes improve reaction speed by up to 20% using adaptive robotic drills. + Training bots allow 3x more touches per minute compared to traditional drills. + Machine-learning platforms track thousands of data points per session—customizing feedback instantly. This isn’t just tech—it’s transformation. Robots are helping players train faster, smarter, and with a grin on their face. #Innovation #Tech #Robots

I made a computer vision app to measure back curvature during deadlift! My previous post focused on measuring deadlift rep timing by tracking the barbell plate. Since then, I’ve added the ability to quantify back roundness, so that we can determine whether the lift was completed with either a straight back or a rounded back. This could be used to help guide proper technique during training. Here are the main technical highlights: — RF-DETR (Roboflow) to segment the person (great performance out-the-box with no additional training!) — YOLO11n (Ultralytics) for bounding box prediction around the barbell weight plate (trained on my own small dataset). — Mediapipe (Google) for pose landmark detection to guide the bounds for the line fitting. — Custom logic to fit a line across the back. I shared this work at AI Tinkerers Raleigh last night and got 2nd place demo! It was great to hear everyone’s feedback and to connect with this inspiring group. I’m excited for how this work can enable AI-assisted feedback and for what could be the beginning of an “AI coach.” I’m very passionate about fitness, and excited to connect with more people in this AI and fitness space. Happy to chat if you have any questions or if you just want to connect! — To explain a bit more about the plots on the right side: The Back Roundness Map shows the deviation of the estimated back curve from a line of best fit. I was very happy to see that the two reps that I intentionally did with a rounded back show up so clearly in the data! The Average Back Shape plot shows how the rounded back reps look visually distinct from the straight back reps. — #computervision #ai #ml #machinelearning #deeplearning #phd #roboflow #google #ultralytics #yolo #gym #fitness #deadlift #aitinkerers Google for Developers Google AI for Developers

For most of football’s history, much of what we watched on the field went unmeasured. Today, nearly every player and ball movement throughout the game is measured, modeled, and analyzed in real time. This data is improving fan experiences and giving them richer sport insights. It's also changing how professionals approach the game—from improving player safety to unlocking new training environments. The results speak for themselves: a 35% reduction in lower-extremity injuries from the redesigned kickoff format, informed by Next Gen Stats data. Innovations like completion probability and rush yards over expectation that make broadcasts more engaging. And now, pose-tracking technology that captures full skeletal data 60 times per second, is opening doors to VR training that could accelerate player development from years to months. I'm proud of how we've expanded our partnership with the NFL on Next Gen Stats, powered by AI tools like Amazon SageMaker and Amazon Quick. What started as a tracking experiment in 2015 has become a critical part of the NFL’s infrastructure that uses machine learning models on AWS to process data from 22 players, generating 500-1,000 stats per play, instantly. What a win for the Hawks last night! If you're still riding the excitement, take a few minutes to read through this deep dive into the science that powers the complex stats you see on screen throughout the season. Cool look at the history of our partnership with the NFL through Next Gen Stats! https://lnkd.in/gX8Mpe7T

AI that counts sheep. Not the kind that helps you sleep. This footage shows AI models counting and tracking sheep with accuracy that would take humans hours to achieve manually. Agriculture is being transformed by computer vision that can detect, count, and monitor livestock at scale. Farmers managing thousands of animals can now get precise counts instantly instead of manual tallies that are always approximate. But the applications extend far beyond counting. The same technology detects health issues by identifying animals moving differently. → Tracks growth rates.  → Monitors feeding patterns.  → Identifies animals that need veterinary attention before visible symptoms appear. This is precision agriculture enabled by AI that can process visual information faster and more consistently than human observation. The technology applies to crops as well. → Detecting disease in plants. Identifying optimal harvest timing.  → Monitoring soil conditions.  → Tracking equipment across vast properties. Agriculture has always been about managing biological systems at scale. AI gives farmers tools to observe and respond to those systems with precision that was never possible before. The revolution is giving farmers capabilities to manage complexity that overwhelmed manual observation. What other industries have observation problems that computer vision could solve at scale?

Computer vision is about to get its second wave. Most people associate it with image classification or AI-generated video. That’s yesterday’s framing. The real shift is happening around world models, systems that don’t just label pixels, but learn structured representations of how the physical world works. Labs led by people like Yann LeCun and Fei-Fei Li are pushing in this direction: → perception tied to prediction → vision tied to action → models that understand space, not just images This matters because: * Robotics stops being brittle * Physical AI becomes trainable, not hard-coded * Simulation → real-world transfer actually works We may be approaching a “ChatGPT moment” for vision, not because of better images, but because models can reason about the visual world. That’s a very different capability.

#MIT's new "Radial Attention" makes Generative Video 4.4x cheaper to train and 3.7x faster to run. Here's why: The problem with current AI video? It's BRUTALLY expensive. Every frame must "pay attention" to every other frame. With thousands of frames, costs explode exponentially. Training one model? $100K+ Running it? Painfully slow. Massachusetts Institute of Technology, NVIDIA, Princeton, UC Berkeley, Stanford, and First Intelligence just changed the game. Their breakthrough insight: Video attention works like physics. - Sound gets quieter with distance - Light dims as it travels - Heat dissipates over space Turns out, AI video tokens follow the same rules. Why waste compute power on distant, irrelevant connections? Enter Radial Attention: Instead of checking EVERY connection: • Nearby frames → full attention • Distant frames → sparse attention • Computation scales logarithmically, not quadratically Technical result: O(n log n) vs O(n²) Translation: MASSIVE efficiency gains Real-world results on production models: 📊 HunyuanVideo (Tencent): • 2.78x training speedup • 2.35x inference speedup 📊 Mochi 1: • 1.78x training speedup • 1.63x inference speedup Quality? Maintained or IMPROVED. What this unlocks: 4x longer videos, same resources 4.4x cheaper training costs 3.7x faster generation Works with existing models (no retraining!) And, MIT open-sourced everything: https://lnkd.in/gETYw8eT The bigger picture: The internet is transforming. BEFORE: A place to store videos from the real world NOW: A machine that generates synthetic content on demand Think about it: • TikTok filled with AI-generated content • YouTube creators using AI for entire videos • Streaming services producing personalized shows • Educational content generated for each student This changes everything. Remember when only big tech could afford image AI? 2020: GPT-3 → Only OpenAI 2022: Stable Diffusion → Everyone 2024: Midjourney everywhere Video AI is next. Radial Attention probably just accelerated the timeline. The future isn't coming. It's here. And it's more accessible than ever. Want to ride this wave? → Follow me for weekly AI breakthroughs → Share if this opened your eyes → Try the code: https://lnkd.in/gETYw8eT What will YOU create when video AI costs 4x less? #AI #VideoGeneration #MachineLearning #TechInnovation #FutureOfContent

Streaming 3D reconstruction is fundamentally a memory problem. How do you map a massive, multi-room environment without blowing up your compute budget as the sequence gets longer? Lingbo-Map just introduced a highly elegant architectural solution to this exact bottleneck: Geometric Context Attention (GCA). Instead of brute-forcing the entire scene history into memory, GCA splits the streaming state into three lightweight buckets: an anchor for global coordinate grounding, a local reference window for dense geometry, and a compressed trajectory memory. By squashing the full sequence history into compact per-frame tokens, the memory and compute requirements remain nearly constant. Running through a DINO backbone, the pipeline actively predicts camera poses and depth maps at ~20 FPS—even on continuous 10,000+ frame sequences. This is how you scale real-time spatial computing and large-scale digital twins without needing infinite VRAM. Models: https://lnkd.in/dxY7D4Ar Project page: https://lnkd.in/dKRUEQaq Code: https://lnkd.in/dXQSJB7u Paper: https://lnkd.in/diPQk3Ki #SpatialComputing #3DReconstruction #ComputerVision #MachineLearning #SLAM #DevRel

One of the latest applications for artificial intelligence could be a game changer — literally. Scientists at Google DeepMind in London have teamed up with the UK's Liverpool Football Club team to create TacticAI, a model that can provide insights on corner kicks. The tool has been trained on a dataset of 7,176 corner kicks from Premier League matches and uses a technique called 'geometric deep learning' to identify key strategic patterns that could prove to be critical in tight matches. "Predicting the outcomes of corner kicks is particularly complex due to the randomness in gameplay from individual players and the dynamics between them," Colin Murdoch, DeepMind's chief business officer, explained on LinkedIn. "TacticAI can model how players interact on the pitch, offering coaches advanced strategies to improve game outcomes." The research was published in a paper in Nature this week. But AI isn’t exactly new to sport, writes Edith Cowan University lecturer Mark Scanlan in The Conversation Australia + NZ. He says it was used in the men’s and women’s World Cups in 2022 and 2023, in conjunction with advanced ball-tracking technology to produce semi-automated offside decisions, and is a powerful tool for organisations. “Professional football clubs have analytical departments using AI at every level of the game, predominantly in the areas of scouting, recruitment and athlete monitoring. Other research has also tried to predict players’ shots on goal, or guess from a video what off-screen players are doing,” he writes. But, while he says AI promises to “offer coaches a more objective and analytical approach to the game”, it cannot make decisions on the fly, which is often where matches are won and lost. What do you think of the use of tech in sport? Could AI assistants give some coaches an unfair advantage or is it the future of competitions? Comment below. By Sam Shead and Cathy Anderson #sport #ParisOlympics Sources:  The Conversation Australia + NZ: https://lnkd.in/g7y35qC4 Financial Times: https://lnkd.in/gY6UENpR Nature: https://lnkd.in/gVsUwJRy

Real-time football analytics running on the MemryX Inc. MX3 accelerator card ⚽ Over the weekend, I built a real-time football analytics pipeline that tracks players, analyzes movement, and extracts tactical insights while running efficiently at the edge. How it works: ✅ Detects all players on the pitch using a YOLOv8 detection model. ✅ Identifies team jersey colors to separate players automatically. ✅ Draws color-coded ellipses around player footprints for clear visual tracking. ✅ Tracks players across frames to estimate movement speed and trajectories. ✅ Calculates camera location using 2D coordinate measurements for spatial analysis. ⚡ Pipeline flow: YOLO PyTorch → ONNX → DFP compilation → Inference on MX3 → Player tracking & analytics 🔗 Check the code here ➡️ https://lnkd.in/djmpYK8A #computervision #edgeai #sports