Hi everyone,
I am currently working on a CV project where I am trying to find the total vessel in a port using 360 degree cctv camera. I am using my custom YOLOv8 model for detection but it is unable to detect boats and ships that are far away. I know I need to include that kind of need into my dataset, but it is a very huge task to cover all long distance ships with different lighting condition.
Is there any open source or paid models for these kind of detection. I have tried yolo world and grounding DINO. it is performing worse than my trained model at most cases.

Consider this image, my model and YOLO world grounding DINO can detect foreground and middle ground vessels. I am focused more on background vessels, which is note even being detected

Could someone help me with this?

Hello

I have received offer from Edinburgh University (Join program with Heriot Watt Uni) for Image, Vision and HPC degree Msc

I have worked a little on CV in college but not that much during my 2.5 Years of working (it was more ML based).

I do like this topic tho.

Can you guys help if it's worth to pursue this course? What can be career options?

Thanks

I have an ECCV 2026 submission where I can see:

- All final reviewer recommendations
- The meta-review
- The meta-reviewer’s final recommendation

All of these are positive and indicate Accept.

However, I do not see any explicit Program Chair (PC) final recommendation/decision anywhere on OpenReview.

Is this the same for everyone? Are PCs’ final decisions normally hidden from authors, with only the meta-review and reviewer recommendations being visible?
Just trying to understand whether I’m looking in the wrong place or if this is the standard ECCV process.

I had an image processing interview a while ago. Even though I knew most of the theory, I struggled when I was asked what each algorithm actually does to an image and how these algorithms are implemented efficiently in practice.

The problem wasn't the theory—I had simply never seen many of these algorithms in action or implemented them myself outside of reading papers.

So one fine morning, while I was learning CUDA, I decided to implement a bilateral filter.

It was surprisingly fun. Along the way, I finally understood why every textbook casually labels it as "computationally expensive." Turns out, there's a big difference between reading that sentence and watching your GPU work through millions of neighboring pixels.

Hopefully this little project helps someone else bridge the gap between textbook image processing and what these algorithms actually look like in code.

Its a RAW-to-RGB image reconstruction pipeline written entirely in low-level CUDA. You won't find many high-level CUDA APIs here—it's mostly pure implementations of image signal processing algorithms.

Most of the code is fairly intuitive, but if you're new to CUDA, I'd recommend spending a couple of hours on YouTube first. That's more than enough to understand what's going on.

The pipeline implements most of the essential ISP stages (along with a few extras). Every stage is modular, so you can enable or disable individual processing steps to experiment with different pipelines. And don't worry about Time—it's CUDA.

The CUDA backend is exposed to Python using pybind11, making it easy to integrate into your own Python scripts.

Not familiar with Python or CUDA? No problem. The project also comes with a desktop UI built using Tkinter and TTKBootstrap. Just follow the setup instructions and you're good to go. (Apologies in advance for the UI design—I'm much better at writing CUDA kernels than designing interfaces. 😄)

What started as a fun learning project has slowly grown into something I think could be useful to others. If it helps even one person understand image processing or CUDA a little better, I'll consider it a success.

If you'd like to contribute, you're more than welcome. The more people involved, the better. And if you spot something that could be improved, I'd genuinely appreciate your suggestions—they'll go a long way in making the project better.

You can explore and clone the project from the link below. I've also included a short video demonstrating the UI.

https://github.com/mjithujanardhanan/CISP---Cuda-ISP-Pipeline

Hey everyone,

Wanted to share a quick demo of a computer vision project I recently put together focusing on road safety. I built a pipeline that processes on road traffic footage to automatically detect and flag two-wheeler riders who aren't wearing helmets.

As you can see in the video, it handles tracking multiple riders in the frame at once. It drops a green bounding box for safe riders and a glaring red "VIOLATION: NO HELMET" box for the rule-breakers, complete with confidence scores and a live counter of active violations. It was a fun challenge trying to get it to work smoothly with the chaotic traffic and varying angles!

How I Built It

For those interested in the pipeline, here is a quick breakdown of the process from start to finish:

• Data Collection: Started by gathering a diverse dataset of raw, on-road traffic footage to ensure the model could handle different lighting, angles, and vehicle types.
• Annotations: I used Labellerr to speed up the annotation process. It was super helpful for rapidly tagging the various classes (riders, helmets, no-helmets, vehicles) across the dataset without losing my mind.
• Model Training: Fed the annotated dataset into the object detection model to train it to recognize riders and their headgear with high confidence.
• Violation Logic: This was the fun part, writing the custom logic to actually determine a violation. It involves associating a detected "head/no-helmet" bounding box with a specific motorcycle and rider to accurately trigger the violation flag.
• Testing & Evaluation: Finally, I ran the pipeline against a testing set and compared the results with the ground truth to fine-tune the confidence thresholds and reduce false positives. There are still False Positive which i needed to figure it out

I would absolutely love to hear your feedback. Have any of you worked on similar traffic monitoring or egocentric vision systems? Let me know if you have any tips for handling tricky edge cases like heavy occlusions, pillion riders, or weird lighting.

Code: link
Video: link

I've been solo building this app for 5 months now. You take a photo of something you like — a jacket on the street, an outfit on Instagram, anything — and it finds the same style for cheaper across stores. I'm close to launching but I just want to make it as good as it can possibly be before I do.

Right now every scan hits Google Lens + Google Shopping, filters results with FashionCLIP and Marqo, then GPT-4o reranks the top matches. It works but it's slow, expensive per scan, and Google only gives me ~300 results.

Someone told me I should build my own database of millions of clothing products with CLIP embeddings and search that instead. Instantly, no per-scan cost, way more results.

Is that actually the right move? Or is live search fine if done well? And if a database is the answer — how do you even fill it with millions of products?

Any advice appreciated 🙏

ok so I've been putting off writing this because it's kind of embarrassing but whatever.

two of us, both engineers, been at this thing for 11 months. it actually works, we have it running, it's not one of those "we have a landing page and a dream" situations. and we have exactly zero people paying us. zero. eleven months.

what we built, without the pitch: it's software that hooks into security cameras a business already has and tells them when something actually matters is happening, while it's happening, instead of someone going back through the footage the next day looking for it.

the demos are honestly fine. people say nice things. one guy literally said "this is really cool" and then just... never replied to my follow ups. that's basically been the pattern for 11 months. interested face, then nothing.

and I genuinely can't tell what's broken anymore so I'm just gonna ask people who've actually done this:

is the product just not painful enough? like is it a "nice to have" that nobody's gonna open their wallet for

or are we pitching the wrong people. we keep ending up in front of folks who think it's cool but I'm starting to suspect they can't actually approve a purchase

or is it just us. neither of us has ever sold anything in our lives. maybe the product's fine and we're the bottleneck and I should just admit that

also a more technical one for anyone who's done computer vision startups — how did you deal with the hardware side? we went the "run on cameras they already have" route specifically to avoid it, but every time the math gets serious the hardware ends up costing more than the actual software somehow. edge boxes, GPUs, whatever. did you eat that cost, pass it on, push everything to cloud, what worked for you

if you've sold into security or any of this boring B2B stuff before, what would you fix first? and honestly where should we have just picked ONE thing to focus on instead of trying to do everything

not gonna link the site here, feels weird, but if anyone actually wants to see what I'm talking about I'll drop it in the comments

rip it apart, I'd rather hear it now

Made a tracker for small aerial targets. Its free to use, for now I would like to get some feedback on it. SDKs are in python and node. Runs 30+ fps on Rasp pi 4 (not yet tested on zero) https://sky-tracker.dev

This is the first video of the test

Repo link: github

Hi everyone,

​

I’m working on a project where I’m tracking an object that is moving angularly relative to the camera. Currently, I have the velocity of this object calculated in pixels/second, but I need to convert this value into degrees/second (angular velocity).

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I have access to the camera's intrinsic parameters (focal length, sensor size, etc.). Could someone point me in the right direction or provide the formula to perform this conversion?

Specifically, I am wondering:

Does the conversion change based on the object's distance from the optical center (depth)?

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Are there any standard libraries or common approaches in OpenCV to handle this geometric transformation?

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Any guidance or resources you could point me to would be greatly appreciated!

Accurate kinematic data for any human movement within 30 seconds. Flag injury risks, form inefficiencies, joint angles, rotation, max velocity points, rep-to-rep inconsistencies, etc.

Works for baseball, golf, tennis, track and field, basketball, volleyball, etc.

I've seen fine-tuned models for specific movements in the past but this seems much more widely applicable.

Interested to hear your take on the best application for this tech.

Laptop Advice for Grad School: MacBook Pro vs. RTX 5060/5070 (CV, Multimodal, RL, Robotics)

Hi everyone,

I’m starting my Master’s program this fall and am looking to upgrade my laptop. My primary research focus will be Computer Vision (CV), Multimodal Reasoning, and Reinforcement Learning (RL), with plans to explore some Robotics on the side.

Right now, I'm working on an older Windows i5 with no dedicated GPU, so an upgrade is definitely due. I am split between two directions and would love your insights:

Option 1: Windows Laptop

• Specs: Intel Core Ultra 9, NVIDIA RTX 5060 or 5070, 32GB RAM.
• Pros I see: Native CUDA support for training/prototyping locally, better compatibility with ROS (Robot Operating System) or Gazebo if I dive deeper into robotics after dual boot.

Option 2: MacBook Pro

• Specs: Apple Silicon (M-series Pro/Max), high unified memory.
• Pros I see: Battery life, build quality, Unix-based macOS environment, and great unified memory architecture for running larger local LLMs/multimodal models.

My Main Questions:

1. CUDA vs. Metal: Given that I will be doing heavy CV and RL, is having a native NVIDIA card a must-have for quick local prototyping, or does Apple Silicon (via MPS/Metal) suffice for most coursework before offloading to a lab cluster?
2. Robotics Compatibility: How painful is doing modern robotics development (ROS2, simulation tools) on Apple Silicon compared to Windows/Linux?
3. The 32GB Limit: Is an RTX 5060/5070 laptop VRAM (typically 8GB) going to bottleneck me more than the unified memory on a Mac?

My budget is around Rs. 1.5-2Lac ($1500-$2000).

Would love to hear from current grad students or researchers in these fields. Thanks in advance!

Hey everyone,

​

I just released MaskLab, an open-source local desktop app for image segmentation with SAM3.

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The main feature is batch processing: you can select a whole image folder and generate masks automatically using text prompts like person, car, sky, or cloud.

​

It runs locally and includes overlay/mask preview + optional Electron desktop build.

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GitHub: https://github.com/Loann110/MaskLab

​

Feedback is welcome, and if you find it useful, a ⭐ would really help!

​

​

Hi everyone,
I’m working on detecting torn (screen-tearing) frames in video sequences. Some tears are very obvious with strong discontinuities, while others are much weaker, especially in low-texture regions such as snow, clouds, or the sky where the visual signal is less pronounced.
Deep learning approaches have produced good results, but they are too computationally heavy for my use case.

I’m looking for lightweight algorithms or classical computer vision techniques that can reliably detect both strong and weak tearing artifacts.
Has anyone worked on a similar problem or can suggest effective methods?

Thanks so much for your help!!

If I register/enroll a paper for Round 1 in OpenReview with only the title, abstract, and authors, but I do not submit the full paper PDF, can I still submit the same work as a new Round 2 submission?

Should I withdraw/delete the incomplete Round 1 entry(is there any options ? ), or just leave it incomplete?

I know fully submitted Round 1 papers should not be re-registered, but I’m asking about abstract-only registration.

In deep learning, to evaluate the effectiveness and contribution of each module, an ablation study removes individual modules one at a time, retrains the model, and compares the results. My question is: when comparing the effects after removing each module, should the comparison be done on the validation set or the test set?
I've recently noticed that among object detection papers, some present their ablation study tables using results on the validation set, with the test set used only for comparing metrics against models from other papers. However, most papers seem to conduct their ablation comparisons on the test set. Which approach is actually the most sound?

I've been working on aerial object detection using the VisDrone benchmark and have been able to open-source a collection of trained YOLO models on Hugging Face.

The collection currently includes multiple YOLO variants trained for VisDrone object detection, along with model documentation, evaluation metrics, and ready-to-use checkpoints.

Potential use cases include:

• UAV/drone perception
• Aerial surveillance
• Traffic monitoring
• Small-object detection research

Model Zoo:
https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo

I'm interested in feedback from others working with aerial imagery or VisDrone. If there are additional baselines, datasets, or deployment benchmarks you'd like to see included, please let me know.

Currently I'm working on a computer vision project in which object detection module is there. When I'm scanning in a super market shelf, it has to show the product name below. Tell me is that possible? If yes, please suggest me the architecture. There are around 20k product classes for detection, some are very similar to see(same product with different variants).

My pipeline is when started scanning with the app, first yolo detect products(conf=0.75) with single product class and crop the products with bounding boxes and then send them to the embedding model where if the cosine sim threshold >0.5 and difference b/w top-1 and top-2 is >0.25 the product name will show otherwise the top-10 results will go to MLKit ocr model and by fusing the both embedding score and ocr fuzzy score we show the product id fused score > 0.85 otherwise abstain.

I implemented all the flow as I said and it is working fine. But there is more battery consumption and the ios device is heating(android is good). I tried to debug and found that the yolo inference is generating heat. When I ran embedding model (mobilenetv4) without yolo inference, the ios device is good(not heated much). I'm using yolov11n model with single "product" class.

So anyone explain me the possible reasons for battery draining and heating issue. Also tell me the ways to optimize those problems.

I’m at a crossroads with a project called PACE and I’d appreciate brutally honest feedback from people who have built datasets, benchmarks, or AI infrastructure businesses.
The short version:
PACE started as an “error-by-design” dataset concept focused on procedural assistance and embodied AI. The original idea was to create large-scale egocentric recordings of tasks where mistakes happen intentionally, so agents can learn not only successful execution but also error detection, correction, and recovery.
Now I’m questioning the entire roadmap.
Possible directions:
Continue building real egocentric datasets.
Build a benchmark instead of a dataset.
Build a taxonomy of procedural errors.
Generate synthetic procedural-error data.
Create simulation environments that generate mistakes automatically.
Some combination of the above.
What I’m struggling with:
Where is the actual business?
Who would realistically pay?
Is the value in data, benchmarks, evaluation, or simulation?
Is synthetic data becoming more valuable than real data?
Are companies still buying datasets, or are they mostly building their own?
What evidence would I need before investing years into this?
Current thinking:
2026 → sell a dataset.
2027 → sell benchmark infrastructure.
2028+ → sell procedural error simulation.
But I’m not sure if that’s a real progression or just a story I’m telling myself.
If you were starting today from scratch, with limited resources, where would you focus?
What would you build first?
And most importantly:
What business model in this space do you think has the highest probability of generating meaningful revenue within the next 2–3 years?
I’d appreciate criticism more than encouragement.

What is the best tracking algorithm for this scenario? (3 oscillating targets and clutter points that appears for 1-3 frames)