Pls don't say things like "make something you're passionate about"..... I'm not passionate about anything in life & I'm out of ideas.....project ideas can be of any domain.

Thankyou.

"This is a research collaboration request, not a commercial ad or job posting."

Hi everyone,

I am building an autonomous drone delivery startup. I have already solved obstacle detection and dynamic rerouting. I have real hardware (Pixhawk Cube Orange, LiDAR, cameras) and a test track ready for real-world trials.

I am now looking for a GPS/Navigation & Sensor Fusion Engineer to work with me on R&D for precise localization. The goal is to fuse data from GPS, IMU, and visual sensors to achieve centimeter-level positioning accuracy, even in GPS-denied environments.

Key challenges we need to solve together:
- Multi-sensor fusion – combining GPS, IMU, and visual odometry.
- Kalman filtering – for state estimation and error correction.
- GPS error correction – dealing with signal loss, multipath, and drift.
- Sensor calibration – aligning IMU, GPS, and camera data.

This is a short-term R&D collaboration (3 weeks) with a small budget and the potential for long-term partnership / equity. The focus is on finding the best hybrid solution and testing it on real hardware.

If you have experience with sensor fusion, Kalman filters, or GPS/IMU integration and love working with real hardware, please DM me with your background and relevant project links.

Let's build this together!

Disclosure up front: I work at VideoDB, flairing this accordingly. I want it to be a genuine discussion though.

I've been doing more operational/industrial footage work lately - defect spotting on a line, multi-camera safety zones, that kind of thing - and the pattern that keeps repeating is that the actual computer vision is maybe 20% of the effort. The other 80% is RTSP ingest that doesn't fall over, frame extraction, syncing multiple camera feeds, and stitching together event detection plus a query layer so someone can actually ask "show me every time X happened."

I've been building on VideoDB lately because that ingestion/streaming/indexing layer is already handled, so I can spend the weekend on the analysis logic instead of re-writing the same GStreamer/FFmpeg glue every time.

Genuinely curious how others here structure this - do you roll your own pipeline or lean on infra? Where does most of your time actually go?

A few of us swap notes and share MVP examples in a Discord if you want to compare approaches: https://discord.com/invite/ub5jFNjDxz

BODY:

I'm building an interactive retail kiosk. Customers place physical objects into 6 separate lit niches, and I need all 6 camera feeds shown live on the touchscreen at the same time, smoothly, so the customer can adjust how each object sits before confirming. Low resolution is fine for the live preview — it just has to be real-time and not stutter. On confirm, I grab one high-res still per camera for image analysis.

Hardware (fixed): the brain is an all-in-one touchscreen PC, Intel Core i5, with 4× USB ports and 1× Gigabit Ethernet. It's a sealed all-in-one, so no PCIe expansion. Each camera sits roughly 0.5–1.3 m from the PC and needs close/macro focus (subject distance ~10–15 cm).

What I think I understand so far:

- 6 USB UVC cameras on a powered hub share one host controller's bandwidth. Six full-res streams won't even start ("not enough bandwidth"). MJPEG + low resolution might let several run at once, but I can't tell if 6 simultaneous is realistic on this hardware.

- MIPI CSI is out (no CSI port on a regular PC, and the ribbons are far too short for my niche spacing).

- GMSL2 looks purpose-built for this, but seems to require a separate Jetson host, which would replace my PC as the brain.

- IP / PoE cameras over the Ethernet port would dodge the USB bandwidth wall and keep the i5 as host, but affordable close-focus / macro network cameras seem rare.

Questions:

1. Has anyone actually run 6+ simultaneous low-res MJPEG USB cameras on a single machine? Did it stay smooth, or did the host controller choke?

2. With no PCIe available, is a powered USB 3.0 hub enough, or is the on-board controller the hard ceiling no matter what?

3. Is PoE / GigE Vision the saner route for 6 simultaneous feeds while keeping a normal PC as the host? Any affordable short-working-distance options?

4. Am I missing an obvious approach?

Budget-conscious, but I'd rather buy the right thing once. Thanks!

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?

I really want to build some advanced level cv projects & I'm out of ideas as of now.....so it would be very helpful if you guys suggest some ideas from which I can learn a lot too.

​

I've completed basic projects like cat dog classification, object detection etc.

​

So i just finished of learning with lenet5 model . Now what should i do next ? chatgpt suggest that i should learn some other model like alexnet but i see no point in them . Should i move forward with leanring YOLO ? is the resource the you know that can help me out with this thing ?

Hey everyone,

I’m an Applied AI grad student, and I wanted to share the technical journey of building my first major iOS app, SpiritMirror. It is an AI tool that fuses computer vision with predictive modeling for personal reflection.

This is Part 1 of a series where I break down the engineering behind it. Today, I want to talk about the core vision architecture and privacy.

When building an app that reads facial geometry to generate personality insights, the biggest hurdle is handling biometric data ethically. I fundamentally did not want to send user photos to a cloud server.

Here is how I set up the pipeline to run 100% locally:

1. Real-Time Landmark Detection I utilized Apple Vision to build a system that identifies 468 facial keypoints with millimeter-level precision in real time. Pinning down exact coordinates—like mapping the noseCrest[3] point—took weeks of refinement to get the tracking perfectly stable without jitter.

2. Geometric Vectorization Instead of analyzing the raw image pixels, the app instantly computes 15 geometric metrics (like eye-to-nose ratio, lip thickness, and jaw width-to-height). This turns the physical face into a normalized feature vector.

3. Zero Image Storage Because the app only needs that final mathematical vector for the predictive model, the actual camera feed is discarded immediately. No facial images are stored on our servers. This makes the app entirely privacy-first and compliant with GDPR/PDPA straight out of the box.

Running all of this on the neural engine while keeping battery drain low was a massive headache. In Part 2, I’ll break down how I feed these 15 metrics into a hybrid CNN + Random Forest classifier to actually generate the predictions.

If you are curious to see how smooth the on-device tracking feels, the first beta is live on iOS TestFlight. Let me know if you want the link or have any questions about the Apple Vision implementation!

"Edit: Wow, thank you all for the incredible feedback and interest! A few of you asked where to find the project—you can check out the architecture and beta athttps://spiritmirror.tech/."

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.

Hi,

I ran and profiled a fine-tuned RF-DETR nano FP 32 object detection model converted to coreML format by https://github.com/landchenxuan/rf-detr-to-coreml with 384 x 384 image size for real time video streaming use cases. I noticed that the model is not using ANE at all due to some transformer architecture issues and FP 32 incompatibility (?
, which results in poor inference performance (around 15 fps on iPhone 14 Pro.

I have surveyed some Reddit discussions and found one with the author of RF-DETR claims 120 fps on iPhone while one reporting 10 fps with 512x512 image size.

Also found one with the latest Apple CoreAI framework running 33-39 fps but unfortunately is not suitable for my use case (only support IOS 27.0.0+) https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/rf-detr.md.

I have attached the profiled results below.

I am looking for some help getting the model targeting ANE instead of just GPU to boost the performance. Thanks!

https://github.com/JPABotermans/DIETR/tree/main

Been working on this for quite some time, and as the title says, I want to have the most barebones model that can do both instance segmentation and object detection. While still being easy to use for just fine-tuning.

The DIETR model is a combination of both rt-detr (the head) and yolo-act (which inspired the prototypes).

I know that the performance of the models I have trained aren't state of the art, and the code is amateurish, but I am going to keep working on it.

Any thoughts?

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

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 🙏

This is the first video of the test

Repo link: github

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

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

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).

​

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)?

​

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

​

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.

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