It tracks the ball throughout the rally and detects racket hits and court bounces. The aim is to eventually turn full match recordings into stats and highlights automatically.

It’s still a work in progress, but I’m pretty happy with how it’s performing so far. I’d love to hear any feedback or ideas for what I should add next.

I’m also looking to test it on more matches, particularly footage where the full court is visible through a wide-angle or fisheye camera—so feel free to message me if you happen to have any.

6 months back me and a friend built this for a client, and I wanted to write up the architecture because the engineering got genuinely interesting, especially the parts that broke.

The idea: you feed the system a base of source videos, it chops them into logical chunks and indexes them by meaning. Then you give it a target video, and it rewrites a new script and reassembles a fresh video by pulling the semantically matching chunks from your base. Whole thing runs through a Telegram bot with a queue.

The pipeline, roughly:

Indexing side: video gets downloaded, cut into logical chunks with FFmpeg + OpenCV (computer vision to detect what's happening on screen), each chunk goes through Gemini embeddings, vectors land in Qdrant. I originally used Firebase for storage and switched to Qdrant later, which turned out to be the right call by a mile.

Generation side:

1. Target video gets transcribed with Whisper v3 large.
2. Transcript goes to Gemini, which analyzes the meaning and writes a new script (could even switch language, english in, spanish out). Script length matched the original within a minute.
3. Script gets voiced through an 11Labs pay-as-you-go provider (way cheaper than native 11Labs at volume).
4. Then the hard part: assembling the actual video from base clips.

The three problems that nearly killed it:

Semantic precision. The model understands roughly what's on screen but can't reliably tell "is this Dota 2 or CS:GO." So instead of picking one clip, we pulled the top 5 matches per sentence and had the model pick the best. Not perfect, but workable.

Clip length. Fixed partly at cut time (nothing shorter than 3s or longer than 15s), but it still crammed too many clips into short spans sometimes. We matched sentence length against clip length to control it.

Repetition, the worst one. The model LOVED grabbing one clip and spamming it half the video, because it scored as the perfect semantic match every time. Fixed it with a hard frequency cap: one clip couldn't repeat more than once per ~5 retrieval passes.

Render time for a 20-min video was about 40-45 min end to end. yt-dlp for downloads (had a funny problem with it I can get into), whole bot ran locally on a server so big file sends weren't an issue.

Honestly the retrieval-by-meaning part was the most fun engineering I'd done in a while. Happy to go deeper on any part. Anyone else built semantic video retrieval? Curious how you handled the "one clip dominating" problem, that one was brutal.

Twelve-class hand-seal recognizer that runs entirely in the tab. MediaPipe Hands gives 21 landmarks per hand, normalized wrist-centered and scale-invariant, into a 126→256→128→13 MLP (~66K params) exported to ONNX Runtime Web. 260KB model, CPU inference at a couple ms a frame, no video leaving the machine. Geometry rather than pixels, which is the whole reason it's this small and this portable.

Reported accuracy is 95.5% on a per-class temporal split — first 80% of each class's frames train, last 20% test. Deliberate: I collected hold-to-capture, so each sample is a burst of near-identical frames, and a random split scatters those bursts across both sides until you're scoring your own training data.

Transitions get their own class. Hands moving between seals pass through shapes that briefly look like other seals, so transit frames were collected as a 13th class using a wrist-motion-gated capture mode. Downstream, a decisive "none" mutes everything; below the veto the real classes compete on renormalized P(sign | not transit).

A parity self-test runs on page load, a known landmark vector through the full JS pipeline, logits compared against the PyTorch reference, regenerated on every export. Normalization drift between training and deployment is the silent killer for landmark models, and this catches it before any prediction is trusted.

Single-subject model, so leave-one-person-out is implemented but the honest "works on strangers" number doesn't exist yet. tiger↔ram confuse at ~14%, genuinely near-identical once you're only looking at joint geometry.

Open to comments or critiques! Repo can be found here: https://github.com/yogendrarau/sealwork

Hey everyone,

I'm planning to build a Computer Vision model focused on action recognition and match analysis in Padel (detecting strokes, player tracking, court positioning, tactics, etc.).

The Setup & Strategy:

1. Baseline Dataset: I plan to fine-tune/benchmark initially on PadelTracker100.
2. Unlabeled Data at Scale: I built a scraper capable of easily pulling 10,000+ hours of broadcast match footage on amateur matches.
3. Semi-Supervised Approach: The goal is to leverage Pseudo-Labeling / Self-Supervised Learning (SSL) on this massive scraped dataset to improve feature extraction and generalizability beyond PadelTracker100.

The Problem:

Most broadcast streams or uploaded matches are capped at 25 to 30 FPS. Since padel strokes (smashes, bandejas, bajadas) involve high-speed racket and ball movement:

• Is 25–30 FPS sufficient for modern DL architectures to accurately classify strokes and tracking?

Has anyone worked on low-FPS fast-action recognition or sports analytics in general? Would love to hear your thoughts, potential architectures, or pitfalls before I start scraping anything.

For example, imagine that I have 2 temporal sequences of images of the same environment both based on the same video. For the first sequence I pick the frame 0, 3 and 5 and for the second sequence I pick 3,5,10. To simplify the problem lets just imagine that I give one sequence to a model he learns from it and produces a lost function and I do the same to the second sequence..

Most likely their output of the lost function is going to be identical because the 2 copied images but what happens in the next epoch? (for simplicity also imagine that we shuffle a bunch of sequences to not overfit) Are the optimizer reducing the step more closely to what he learned from the 2 sequeces? How does he link contexts? Does it even link contexts?

Title

Best workflow for longitudinal rigid registration and landmark tracking on serial STL meshes?

Hello everyone,

I am a periodontist looking for advice on the most practical computer-vision workflow for analysing serial 3D dental surface scans.

We have five STL meshes of the same patient’s lower dental arch:

• T0: before treatment
• T1: immediately after treatment
• T2: 1 month
• T3: 3 months
• T4: 6 months

The region of interest is the six lower front teeth. These teeth were connected with a wire-composite splint, and we want to measure whether their positions change over time.

A simple analogy is a fence. Imagine six central fence boards whose movement we want to measure, while several boards farther to the sides are assumed to remain stable. Each new scan must first be aligned using only the stable side boards. The six central boards must not influence the registration, because their movement is the outcome of interest.

In our case:

• the six anterior teeth are the moving fence pieces;
• selected posterior tooth surfaces are the stable registration reference;
• the rigid transform calculated from the posterior surfaces should then be applied to the complete follow-up mesh.

The anterior teeth must also be excluded because the splint is added after T0, changing their surface geometry independently of any actual tooth movement.

We will attach two screenshots. The first shows one predefined measurement landmark on each anterior tooth and the posterior reference regions. The second shows a fixed baseline curve passing through the six T0 landmarks. The landmarks, reference regions and curve are already defined; we are seeking advice only on how to implement the analysis.

The required workflow is:

1. rigidly register each follow-up STL to T0 using only the selected posterior surfaces, with rotation and translation but no scaling;
2. apply the resulting transform to the complete follow-up mesh;
3. record the X, Y and Z coordinates of the six predefined landmarks at every timepoint in a fixed coordinate system;
4. calculate ΔX, ΔY, ΔZ and total 3D displacement relative to T0;
5. calculate the shortest 3D distance from each follow-up landmark to the fixed T0 curve;
6. export the results to CSV for statistical analysis.

Ideally, we would also export the transformation matrix and a registration-error measure calculated over the posterior reference surfaces.

We have already tested ICP-based alignment in Medit Design and a custom Open3D application, but we do not yet have a complete and reproducible workflow for registration, landmark collection, point-to-curve distance calculation and data export.

Our main questions are:

1. What software stack or workflow would you recommend for this task?
2. Would Open3D/Python be a sensible approach, or is there an existing tool that already handles most of this reliably?
3. How would you structure the pipeline so that it can later be repeated consistently across multiple patients?

We can provide one fully de-identified example dataset if someone is willing to assess or demonstrate a possible workflow.

Thank you for any practical suggestions, example code or references to similar projects.

Considering a 4DGS rig; I know I need enough USB controllers & PCIe lanes, but even something like this: https://www.bhphotovideo.com/c/product/1442368-REG/sonnet_usb3c_4pm_e_allegro_usb_c_4_port_pcie.html only really needs PCIE 2.0 x 4. So can I really go rock bottom with these components and have it still work as long as I have a fast enough NVMEs?

cameras
https://www.baslerweb.com/en-us/shop/a2a1920-160ucbas/

I have no experience with image processing and I am trying to vibe code a tool to replace backgrounds in used-car listing photos for a family member who owns one dealership.

Two requirements: (1) the car exterior and interior must stay pixel-identical — no regeneration or distortion, and (2) background visible through windows needs replacing too.

Generative models (GPT-image-2, Nano Banana) solve the window problem but subtly alter the car — paint tone, reflections, distorted text on plates/displays, occasional distortion on unusual angles.

Segmentation models (SAM2, BiRefNet) preserve the car
perfectly but treat glass as solid — they don't flag the
background bleeding through windshields/rear windows as background at all.

Has anyone solved this specific combination? Preferably with API access which I can incorporate into the workflow.
Background image is also provided as input.

Hey all ! Is anyone else going to be at KDD in Jeju? Would love to connect with fellow attendees.

I work on interpretability, fairness, and editing of text-to-image models, so I'd especially love to meet people working in these areas. But honestly, we can chat about anything: research, the conference, life, or grab a coffee/drink.

I land in Jeju on the night of the 8th of August, so hmu if you're around and want to link up!

I’m trying to train an object detection model for a specific object. I trained it on a custom dataset collected which focused on outdoor and indoor environments and mostly LED light. But as soon as I run the pipeline on inference (edge device) in a completely different environment (indoor and fluorescent light) the detection does not work efficiently which makes sense as the dataset did not contain that specific scenario. But let’s say if I want to run this for a commercial product which could consist of different lighting and environment settings, what could I do in the training pipeline or dataset to make the detector generalized to all different settings?

Would appreciate any advice for people working in this field with edge device constraints.

Hi Guys,

So I have been working on document layout analysis for some time now. I have tried the models like Doclayout, Docling, Miner U, marker.

Overall Docling performs well, but the problem is that it over performs. And mineru u misses some content like the corresponding author on the page-footer. And it is also missing the masthead mark, and the article-type label.

In my opinion unlimited OCR performs well in all the tasks, but in general it is failing to recognise any style at all. And it is bad at recognising logos.

So I am wondering are there any state of the art models (SOTA) that are good at PDF text extraction and layout extraction ?

Thanks

Hey,

I'm building a photosphere rig - for that I was looking into good industrial cameras, since they need to meet the following criteria:

• Global Shutter
• At least 20MP (Most seem to cap out at 24MP)
• At least 10FPS
• Since this will be build in a cluster of multiple cameras, time sync, so they all fire at the same time
• Color (they will be used outdoors in varying lighting condition)
• Need software control over things like ISO, Exposure etc.
• Preferably connected via Ethernet/Coax (not USB)
• Good API to interact with them via C/Rust

Brands I looked into (they are very expensive tho)

• LUCID Vision Labs Triton2
• Compare Basler ace 2 a2A5060-35cc
• Teledyne Blackfly S USB3: 24,5 MP, Farbe, C-Mount
• Alvium G5-2460 Color

I will require ultrawide lenses on the cameras So to cover around 100-100 degrees FOV.

Does anyone have experience with these brands/models? Are there even high res options available? Any cheaper alternatives, perhaps some Chinese brands etc.

Hi everyone,

I’m attending ECCV 2026, and my university requires an official day-by-day conference schedule for travel approval.

The official website currently lists the conference dates, but the detailed technical program has not been released yet. I understand that the exact release date may not be known, but based on previous ECCV editions, does anyone have a rough idea of when the detailed schedule is usually published?

Thanks in advance!

Thin structures are where dense vision systems usually give themselves away. A cable gets swallowed by the background. Two nearby objects turn into one blob. Good average segmentation numbers do not say much about those cases.

LingBot-Vision uses masked boundary modeling in its public release and provides several ViT/16 backbones, including a roughly 1.1B version. The basic question is whether those features keep the boundaries when a small decoder is attached. Test a frozen or lightly tuned backbone on boundary errors, depth breaks, and small objects. If it only wins on a PCA plot, that is a much smaller result.

Hi everyone,

A house in my area was deliberately set on fire, and I have several videos of the incident. One of the videos shows a vehicle leaving the scene, but the license plate is too blurry for me to read. I hope one of you can track the number

Working on a real time motion detection library camera feeds where the camera itself moves (Pan Tilt Zoom + Translation).

My current pipeline:

• shi-tomasi corners + LK between consecutive frames
• findHomography with RANSAC to estimate camera motion
• warp the previous frame, absdiff against the current one
• Farneback dense flow on top for extra filtering
• then a pile of heuristics (edge density, flow magnitude deviation from median, contour solidity) and temporal confirmation over a few frames

Everything is CUDA accelerated, runs at 576x324 internally, speed is fine, quality is not.

The failure modes I keep fighting are false positives on anything with strong edges when the camera moves, and missing objects that move slowly or are small.

Things I've already looked at:

I tried fastMCD (https://github.com/vcg-uvic/fastMCD) - Didn't work well for me at all, and in hindsight it uses the same homography compensation I already have so maybe that's not surprising.

I looked at Segment Any Motion in Videos. It looks great but needs the whole clip up front so it's useless for streaming.

I've also been wondering about point tracking methods like CoTracker or TAPIR, since accumulating displacement over a longer window seems like it would help with the slow/small object case. But I don't see how to get those running in real time in C++.

Questions:

1. Is my current pipeline the right approach?
2. Is there anything in this space that actually runs real time that I've just not heard of?
3. Anyone had luck with running a point tracking model like CoTracker in C++, real time?

Hi all,

I am trying to make a custom depth map tracking rig. I was going to use

2 a2A2464-115g5mBAS Basler ACE2 2448 × 2048, 115 fps, from basler with
Basler 25mm, C23-2528-16M, 2/3” or Basler 50mm, C23-5028-16M, 2/3” for lensing so I can resolve fine features of rodents.

I am going to have a baseline of 40mm and everything can be processed offline. I just wanted to know if I am on the right track for getting sub millimeter resolution depth maps.

Thanks in advance

Join us on July 29 for the monthly MCP, Agents and Skills Meetup!

Register for the Zoom.

Talks will include:

• The Agent Control Plane: Turning Coding Agents into Reliable Engineering Workflows - Alex Lavaee at Atomic
• UISurf: Toward Universal UI Automation with Cross-Environment Agents - Henry Ruiz at Texas A&M AgriLife Research
• Building Safe Agent Sandboxes: Let Agents Act Without Breaking Production - Adonai Vera at Voxel51
• From Manual Workflows to AI-Assisted Skills: Building Reliable Internal Automation - Janvi Vijaykumar Saddi at Astreya Partner

Tried template matching. Tried segmentation SAM2

We’re hosting a live discussion with Bob Flores, former CTO of the CIA, on the current state of video intelligence and computer vision in real-world security environments.

The conversation will cover:

• Where existing video analytics still fall short
• The gap between model performance and operational deployment
• Scaling across large numbers of camera feeds
• What users in security, defense, and intelligence actually need
• Where the field is heading next

This is intended as a market and technology discussion, not a product demo.

July 30, 12:00 PM ET
Registration: [https://luma.com/yr8fhxt7]()

Curious to hear what people here see as the biggest unsolved challenge in video intelligence today.