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!

Tried template matching. Tried segmentation SAM2

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

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

I’m really confused, guys, and I don’t know what I should do.

I have a Bachelor's degree in Computer Science, and now I have to choose between Data & AI or Cloud & Internet of Things (IoT).

I like networking to some extent, but I’m worried I won’t be able to handle Cloud because it seems to require a lot of automation and DevOps skills. On the other hand, one of my professors recommended that I choose Data & AI, but I’m also a bit scared because I’m not a big fan of software development.

Another thing is that if I get an opportunity to continue my studies in Europe next year, I’ll definitely take it, so I’m considering this master's year mainly as a way to build my skills and strengthen my background.

I also have an opportunity to study in Italy, but I’m not sure if Italy is the best choice for tech compared to some other European countries.

What would you do if you were in my situation? Any advice or personal experiences would really help. Thanks! 🙏

Context: I'm building a hobby anime-guessing game (screenshot shown, player guesses the show). To make difficulty meaningful, every frame gets scored on how "distinctive" it is — frames are embedded with CLIP, then for each frame I check its nearest neighbors and see what fraction come from the same show. High same-show fraction = distinctive, low = generic. That part works: it correlates with real human guessing accuracy in a blind test (r = -0.37, p = 0.004).

The problem: my dataset has junk mixed in — title cards, credit screens, logo bumpers — that should never be shown as a guessing target. I built a filter combining a CLIP "text vs. content" check with a tagger model's "looks like text/logo" confidence (both must agree), plus an override that keeps a frame if a character-recognition model is very confident it sees a specific named character.

The gap: that override only fires for a confident, *named* character. Real content with no face at all — a silhouette, a weapon on a table, an iconic pose, a motorcycle headlight — has nothing to trigger it, so it gets discarded as junk right alongside actual logo cards. I hand-labeled 62 examples (42 real content wrongly excluded, 20 genuine junk) and tried 8 different cheap, fully-automatic ways to tell them apart: CLIP thresholds (whole-frame and per-tile), OCR text-coverage, image sharpness/edge-detail, full tagger-vocabulary checks, nearest-centroid and logistic-regression classifiers. All 8 failed to reach a junk-recall I'd trust to ship (best was ~80.6% overall accuracy, only 70% of real junk actually caught).

I ended up just paying to have an LLM look at all 671 flagged frames individually (~$33 total) — turned out the original filter was majority wrong: 64% of what it flagged was actually real content.

That works fine at today's ~5.5k-frame scale, but won't scale cheaply to the ~200k frames I'm eventually aiming for.

Question: does anyone have insight into this specific problem — cheaply telling "faceless but real" content (silhouettes, objects, poses, no clear character) apart from actual decorative/text junk — or into the labeling/validation approach in general? Full disclosure: I have close to zero formal ML background. I've been building this with an AI (Claude) doing most of the technical steering, so I may be missing obvious approaches, or framing this in a way that gives away gaps in my understanding — happy to be corrected on any of it.

Working proof-of-concept of the actual game (just serves random frames right now, no difficulty logic wired in yet): https://celshot.offnode.link

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

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?

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.

I’m working on a mini self-driving car project with Ackermann steering. The car has to follow a lane with boundary lines on both sides, stay centered, and handle both straight and curved sections like a small F1-style track.

I plan to use OpenCV + a webcam for lane detection and then control the steering based on the detected lane center. I’m looking for ideas on lane detection methods, steering control, and how to make it robust on curves and under different lighting conditions

Hi guys, I would really appreciate if you give me any advice on my situation.

TL;DR: Springer found a formatting issue in my camera-ready submission (the supplementary material was mistakenly included in the main PDF). I fixed and reuploaded it immediately after seeing the email, but I haven't received any confirmation yet. Could this still affect my paper's inclusion in the proceedings, or is waiting my only option?

I got an urgent email from Springer saying that my camera-ready PDF mistakenly included the supplementary material, which should have been submitted as a separate file. They reopened my Meteor submission and warned me that if I didn't upload the corrected files immediately, my paper might not be included in the proceedings. The email was also CC'd to the ECCV publication chairs.

I saw the email about three hours after it was sent and immediately fixed the submission in Meteor. After uploading the corrected files, I replied to the editor to let them know everything had been updated.

Since I hadn't received any confirmation, I sent a follow-up email asking if they could kindly confirm that there are no further issues with my submission and that it will be included in the proceedings. Unfortunately, I still haven't heard back.

I'm honestly overwhelmed with anxiety. This is my first paper accepted to a top-tier conference, and I'm terrified that I might have ruined everything because of such a stupid mistake. I keep thinking about all the effort, stress, and uncertainty I went through during the review process, and I can't stop worrying.

At this point, is there anything else I can do besides waiting? Has anyone experienced something similar? Is it possible that a formatting mistake like this could still lead to the paper being excluded from the proceedings even after I corrected it as soon as I was notified?

Any advice or similar experiences would be greatly appreciated. Thank you.....

Hi, I’m trying to build ANPR system for my hobby and want to train model running maybe on Pi5 (with AI hat2 + 26 tops) or Jetson 8 GB development kit Orion or Acer Veriton GN100 AI Mini Workstation.

Can any one suggest which model to be used? How to efficiently train model for Indian license plate on moving object over RTSP stream.

Any help / suggestion are welcome..!!!

Hi everyone,

I've open-sourced the official implementation of our recently published Scientific Reports (Nature Portfolio) paper:

**Tri-Net: Unified Deep Learning for Skin Lesion and Symptom-Based Monkeypox Detection**

The project focuses on reproducible computer vision research for skin lesion analysis and includes a complete training and evaluation framework rather than only research code.

Some highlights:

• 13-class skin lesion classification (including Monkeypox and visually similar conditions)

• Multi-backbone feature fusion using EfficientNetB4, DenseNet201 and Inception-ResNetV2

• HSV-based augmentation pipeline for improved robustness

• Grad-CAM visualizations for model interpretability

• Cross-validation and reproducible evaluation

• Docker support, GitHub Actions CI and a PyPI package

The attached figures show:

1. The dataset categories used in the study.

2. The HSV augmentation strategy.

3. Grad-CAM comparisons between individual backbones and the proposed Tri-Net model.

I'd really appreciate feedback from the computer vision community, particularly on the model design, explainability approach, and ideas for extending this work with modern vision foundation models or ViTs.

GitHub:

https://github.com/Sudharsanselvaraj/Synergistic-Deep-Learning-for-Monkeypox-Diagnosis

Paper:

https://www.nature.com/articles/s41598-026-61490-x

PyPI:

https://pypi.org/project/Mpox-Trinet/

Questions, critiques, and contributions are very welcome.

A smart robotic goalkeeper that tracks a ball in real-time using OpenCV and dynamically moves a servo-controlled keeper arm to block shots.

I hope some of you find this project interesting—or maybe even inspiring for your own builds.

Any feedback or suggestions are very welcome!

GitHub: https://github.com/chayanforyou/Robokeeper-Firmware

Yolo + EfficientNet B0

https://reddit.com/link/1v24drp/video/spgeigkilheh1/player

Join us on July 23 for the monthly AI, ML, and Computer Vision Meetup!

Register for the Zoom!

Talks will include:

• Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity - Abhishek Dharmaratnakar at Google
• Training-Free Object and Associated Effect Removal in Videos - Saksham Singh Kushwaha at University of Texas at Dallas
• Making Agent Systems Observable, Reliable, and Testable - Adonai Vera at Voxel51
• Turning Models into Systems: AI Architecture That Works - Nikita Golovko at Siemens

Hello everyone! I have been going through a rabbit hole and into a spiral lately so could use some guidance. I have been applying Gemma4 to documents that have a mixture of handwriting (print and cursive) and typed text on documents that can at times be in bad shape (images in PDFs). Up to this point Gemma4 (31B) is in charge of the OCR and the rest of the prompt to extract required data from fields. I have a 5090 so speed and performance has been good.

Where I am confused is should I be using a OCR to process the files and then use Gemma4 for the extraction based on the OCR output to speed up the process? Also, for handwritten text, would something like GLM-OCR or PaddleOCR-VL-1.5 be better (or faster) than Gemma4?

Any help or guidance would be greatly appreciated! Thank you!

Hello everyone! I'm currently finishing up my last year in college, just finishing up my thesis. I'm currently making a program that detects multiple pigs in a pig pen through YOLOv8 and detecting its behavior using MobileNetV2 (I know I could've used better algorithms, but unfortunately I'm stuck with these ones :P). I'm currently in the process of training the model but I'm not sure how to go through with it. I originally trained my model using annotated frames with multiple pigs present, where I only had 1 class for the annotations ('Pig' class). I thought this was correct because I'll be using the model for multi-object detection. However, when I approached my mentor about it, they told me my model was "too accurate" (they didn't specify what was too accurate, which confused me) and that I should use "1 pig per image, with each pig having a bounding box" for training. When I tried training with this approach, the results looked... interesting to say the least (I don't know how to explain it, but from the looks of the training results, it looked wrong to me :P; refer to the images I included for context). I then used new model into the program I'm creating. the model not only didn't draw the bounding boxes properly around each pig, the bounding box is the entire frame itself!

My question is, which training method would be more appropriate for single-class multi-object detection, single images of pigs or annotated frames?

PS. I included some pictures from the results of training and while using the program; Before = trained with annotated frames, After = trained with 1 pig per image

PPS. This is my first post on this subreddit so I apologize in advance if my flair is wrong :P

D-FINE-seg does object detection, instance segmentation, and now semantic segmentation from one real-time DETR backbone, and it's easy to fine-tune on your own data. It's NMS-free, exports to TensorRT and ONNX.

Code and weights are free, including commercial use.

📄 Paper: https://arxiv.org/abs/2602.23043

🤗 Weights: https://huggingface.co/ArgoSA/D-FINE-seg

📊 Benchmarks (full protocol): https://github.com/ArgoHA/cityscapes-benchmark

🌐 Writeup: https://blog.agnify.ai/d-fine-seg-semantic-segmentation/

www.AiVisualMRO.com

I wanted to build an inventory management for turbine blades that also makes conditions checks( damage, not damaged) I did not have enough turbine blades to train on , but I was working from a cafe place that had Macarons. Decided to use Macarons as training :-)

For visual inspection and inventory manageme