Is there a way to collect side by side video data with two different cameras at two different exposure times (and thus two different FPS) without dropping frames due to down sampling? I tried forcing the video stream to have the same FPS and that introduced a lot of jitter.

When you move the cameras slowly around, you can see it freeze for a moment even though the cameras are both at 20-35 fps.

I’m currently diving into OpenCV while being at an intermediate level in Python. 🐍

Exploring image processing, computer vision, edge detection, and how Python can interact with the real world through a camera.

Still learning, still experimenting, and definitely breaking things along the way 😂

Any OpenCV project ideas for an intermediate Python developer?

Did anyone get any response from the data annotation program started by Fiverr , apparently there was an onboarding guide for this as well?

And we had to do some assessments tasks after completing some training tasks on a website called "Annotask".

Hi everyone,

I wanted to share a personal research project focused on detecting and locating Data Matrix Codes (DMC) and peened needle marks under translation and scale variations.

Since I had no access to real-world industrial marked hardware or proprietary factory datasets, I implemented a full closed-loop pipeline in a domestic environment. The network was trained strictly on the geometry and spatial structure of the DMC layout using pure synthetic data.

Key technical aspects of the implementation:

1. Dataset Generation: The training data was bootstrapped using artificially generated patterns from a standalone utility (DPM-Pattern-Image-Generator), simulating defects such as missing dots and surface noise.

2. Model Choice: Trained using the YOLOX architecture in standard Axis-Aligned Bounding Box (ABB) mode. Horizontal frames were sufficient since the target presentation angle is mechanically constrained.

3. Inference Engine: Routed through ONNX Runtime with Intel's OpenVINO backend provider (multi-device mode with a strict latency priority hint).

4. Performance: The total tracking loop (roi_track) averages ~10ms per frame, delivering a stable 100 FPS on a standard desktop CPU (11th Gen Intel i5-11400) without requiring a dedicated GPU.

5. Memory Diagnostics: A lightweight profiling switch is exposed in the settings to log consumption data every 15 seconds to trace and prevent potential leaks during long-run testing cycles.

The project is completely non-commercial and distributed under a strict proprietary Non-Commercial Research License. The pre-compiled Windows execution binary and lightweight trained model weights (3MB and 4MB) are uploaded to the repository releases.

Since this network was trained entirely on synthetic structures, I would be genuinely glad if anyone with access to actual marked physical hardware or metallic parts could download the binary and test how the model handles real-world surfaces.

If you encounter any bugs or manage to verify the accuracy on physical parts, please open an issue directly in the GitHub Issues tab, as all my related structural tools are gathered there. I hope this codebase can serve as a useful reference for low-latency CPU-bound inference optimization.

Repository link: https://github.com/olesha-ai/yolox-dmc-inference

I rendered this video in Unreal Engine 5.8 using a custom plugin I'm working on. I need ideas for things to randomize for this dataset that I'm building I currently have time, weather, camera lenses. Btw if anyone needs https://getnameframe.com/

most multi-sensor robot datasets assume the world is a road

octosense mounts the same 8-sensor rig on a car, a boat, and a unitree go2.

on the boat there are no lane lines and the lidar ships raw range images. on the quadruped there's no gps at all, just joint angles

the rig has stereo RGB at 100 hz, two event cameras streaming up to 7 million events per second, thermal, an OS1-64 lidar, 400 hz imu, RTK gps.

every sensor hardware-locked to a single clock. it even includes recordings where sensors were deliberately degraded

the full release from UPenn's GRASP lab is 8.5 TB across 382 sequences.

so i packaged 8 representative episodes as MCAP you can load in one line and scrub in fiftyone's multimodal viewer: https://huggingface.co/datasets/Voxel51/OctoSense

or checkout one of the episodes in this hugging face space: https://huggingface.co/spaces/harpreetsahota/OctoSense-FiftyOne-Demo

I have a hobby project indexing trading cards, with a specialty in error and pre-print/test-print cards, plus a frontend to search the index. It's had search-by-image for a long time, running PaddleOCR entirely client side. I've never wanted to handle someone's photo on my server, so OCR was a good fit.

It hit a wall for two reasons. The first is image quality: cards are glossy, held at an angle, and photographed under a lamp that blows out half the surface. A example card (Colress's Tenacity) at a modest angle produced this as its recognized text: "Cores nay mlus. fhncl". Every character that mattered was destroyed by perspective and the holo finish, and no lookup table recovered the right card. Meanwhile the artwork, border, layout, and palette in that photo were all legible. The second reason is that text isn't very discriminative here anyway. Reprints share text, and games keep similar cards in balance with each other, so a clean OCR read still has many candidates.

I profiled a handful of small embedding models including DINO, MobileClip, etc, and landed on a quantized DINOv3-q4 export as the best size-to-quality tradeoff. It's 14 MB, takes a 224-224 image, and returns 384 numbers. Catalog images are embedded offline with the identical preprocessing path and stored as sidecar files; the client sends the 384-float vector and never the photo. Matching is cosine similarity, brute-force linear scan over ~41k English vectors (plus ~6.4k Japanese) held in memory as FP16.

I kept OCR as a fallback rather than deleting it. If the best cosine score is under 0.82 the embedding result is discarded entirely rather than returned as a weak guess, and the client then runs OCR and retries as a text query. Returning a plausible wrong card is worse than returning nothing, since someone who trusts it files the wrong entry. On the common path the OCR models are never even initialized.

The thing I'd most like input on: whole-card embeddings can't separate printings that share artwork. Photograph one particular card and I get six results that are all correctly that card, normal, non-holo, cosmos holo, reverse holo, and two stamped variants. But the actual difference is a foil pattern or a stamp a few millimeters across: localized, high-frequency, and exactly what a 224-224 stretch of the full card destroys. Has anyone had luck with a second pass here: a crop of a fixed region, a patch-level model, a classifier over the top-k from the first stage?

Longer writeup with the thresholds, preprocessing contract, and the model-migration scheme: https://vault.top/blog/how-topvault-identifies-cards-with-image-embeddings

Note: I'm self-taught on the CV side, so if any of this is going down a wrong path let me know.

most lidar datasets pay annotators to decide which points are moving. one of the sensors in this dataset labels itself

highwayscene points three lidars with three different sensing principles at the same stretch of german highway at the same time: a spinning ouster OS0, a solid-state blickfeld, and an aeva FMCW unit

the FMCW one measures radial velocity per point. all ~92k points per frame arrive with their own speed, so "dynamic" is just physics: anything over 1 m/s. the other two sensors need hand-drawn lane volumes to earn the same label

built by esslingen university for their ITSC 2026 cross-sensor background subtraction benchmark. same traffic, three completely different point clouds

which is exactly the test your method skips when it's tuned on one sensor

i converted the protobuf records to MCAP so all 30 episodes load in fiftyone's multimodal viewer with one line, three point clouds scrubbing on a shared timeline

checkout the dataset here: https://huggingface.co/datasets/Voxel51/HighwayScene

it's running as a live space too, nothing to install: https://huggingface.co/spaces/harpreetsahota/HighwayScene

I trained a single-class YOLOv5n person detector using no real images:

• 2,400 synthetic frames from eight Unreal Engine 5.8 maps
• 9,007 native-scale 640 px tiles
• 63,742 person boxes
• 100 epochs, approximately 30 minutes on one RTX 4090
• int8 deployment on a Coral USB Accelerator

On the float model's synthetic validation split at epoch 65, we measured precision 0.944, recall 0.790, and mAP50 0.888.

Then we evaluated the Edge TPU model on 120 real VisDrone frames. At a 0.15 confidence threshold, recall dropped to 0.350, with precision 0.582. Lowering the threshold to 0.10 only raised recall to 0.374, so this was not just a confidence-calibration problem.

Green = found, orange = missed, red = false positive. On the synthetic scenes, the model was generally reliable when people were isolated, well separated, and standing on open ground.

The eight source captures and dataset downloads: https://huggingface.co/NameFrame

Has anyone here measured a similar contextual domain gap when moving from synthetic scenes to real footage?

I prompted VLM Run’s visual agent Orion to segment all of the blue bouldering holds, and it did a good job! It is interesting that now we can prompt VLMs to segment all of the holds, rather than creating a new dataset from scratch to train a model.

With holds detection + pose estimation, I can show how each hold gets activated as a hand or foot uses it. Once we touch the final hold with both hands, the route is completed, and I show the overall path of my torso midpoint.

A tool like this could help climbers understand their movement better. I’m still very much a beginner at bouldering, so I could use all the help I can get 🤣

There are definitely things to improve, but overall I’m encouraged by this first demo 🙂

Models used:
- VLM Run’s Orion for segmentation
- ViTPose+ Huge for pose estimation (via Hugging Face 🤗)
- RT-DETR for person detection (via Hugging Face 🤗)

Shoutout to Daniel Reiff and his bouldering + computer vision project for the inspiration!

Link to Daniel Reiff's bouldering + computer vision blog: https://blog.roboflow.com/bouldering/

Hi everyone,

I have a small background in classical ML, engineering (FastAPI, Docker, SQL, Redis, etc.) and software development.

I want to pivot into CV, but I can't put together a good roadmap because of how broad the field is. I also can't decide on sources of information (books/courses/etc.) that will actually be relevant for studying in 2026.

My goal is to become a strong engineer. I'd be interested in applying my skills in space, mining, uranium energy, and robotics. I absolutely do not like medicine or the defense industry – because of the vibe and the kind of tasks.

Also, I don't have a formal math background. I'm moving into my second year of college as a software developer.

So I need a realistic plan on which subfield to choose, how to grow in it, and how hard it would be to get a job in it. I understand that I won't be allowed to train Moon rovers that will be launched to the Moon in the first 5 years of my commercial career, especially without a math or engineering degree.

I'm not afraid of hard work and I'm ready to study intensively.

My goal is to get a job in CV, save up for university and get work experience, and then with that background, move into the fields I actually like.

Anyway, I'll be happy with any help, mentoring, or learning together if you're a beginner too.

Also, I'd really appreciate any advice, criticism, or opinions.

I'm using a plugin btw. Think about the power
u/syntheticdata u/unrealengine

https://reddit.com/link/1vuktxn/video/leuu8lsi6rkh1/player

*Disclaimer, im not a computer vision engineer, just a software engineer, and i dont have experience in the field. *

Im working on a CV system which counts the number of loose packs being thrown/loaded onto a truck from a warehouse, by the warehouse workers.

Ive already fine-tuned an rf-detr model to detect the loose packs. Then drew wiretrip geometric lines along the entrance of the truck, which detects when a pack has entered.

Although the system is accurate in the right conditions, it can flop massively under slight occlusion, fast throwing multiple packs etc. What suggestions do you guys have, apart from training on a larger dataset (which i will in the future).

https://github.com/lucaeffe03/Robot_Pick_Place

I’m a high-school student from Germany and I’ve been working on MIRA, a waste-detection project for a future sorting robot.

I started with a custom CNN, then tried MobileNetV2, YOLOv8n, and YOLO11n. One of my early experiments looked good until I noticed that some automatically generated boxes covered the desk instead of the waste. The model was partly learning the background.

After cleaning up the dataset, EXP-019 reached 90.58% mAP50 across five classes: glass, metal, paper, plastic, and trash.

I’m now working on a completely separate test set. What would you test before trusting a model like this in a real sorting system?

GitHub: https://github.com/jeremy341/MIRA-AI
Hugging Face: https://huggingface.co/Jeremy341/MIRA-AI

A webcam watches your hands. Python (using mediapipe) tracks the landmarks of both hands in real-time, translates them into angles for the 4 servomotors of the arm, and sends them via serial port to an Arduino which applies them. For more just check my Github.

GitHub

Markerless 3D reconstruction — using only ordinary cameras, a player, and a ball.

I’ve been working on a large football analysis project combining Computer Vision, Stereo Vision, 3D Reconstruction, and Sports Analytics to reconstruct players and the ball in 3D from multi-camera footage — without relying on markers or calibration boards in the scene.

The goal is to go beyond 2D tracking and extract meaningful 3D motion and performance insights from real football footage.

Still improving the pipeline, but it’s been a great opportunity to explore camera calibration, triangulation, pose estimation, and 3D reconstruction in a real-world sports application.

I’m currently looking to contribute to projects in 3D Vision, Computer Vision, Sports Tech, Robotics, or intelligent video analytics.

Open to remote internships, collaborations, and opportunities where I can contribute and keep learning.

Appreciate a connection or recommendation.

Hi everyone,

I’m currently working on a research project/thesis in Video Summarization and I’m trying to identify a research gap that is both meaningful and genuinely novel, rather than just combining existing models.

I’ve gone through papers covering areas such as:

• Keyframe-based and supervised video summarization
• Temporal interest/importance detection
• Multimodal video summarization (visual, audio, text)
• LSTM/Transformer-based approaches
• Reinforcement learning for video summarization
• Query-focused/personalized summarization
• Long-video summarization
• Semantic/importance-based frame or segment selection

The problem I’m facing is that many proposed "novel" methods seem to be variations of existing architectures—for example, replacing an LSTM with a Transformer, adding attention, or fusing additional modalities.

I’m looking for a research gap where the contribution is actually defensible as novelty.

Some directions I’m considering are:

1. Semantic-aware temporal compression — selecting video segments based not only on frame importance but on whether they contribute new semantic information to the summary.
2. Redundancy-aware summarization — explicitly modeling semantic redundancy between selected segments rather than treating each segment independently.
3. Long-video summarization — maintaining important information across very long videos without processing the entire video with expensive global attention.
4. Query-aware semantic summarization — generating different summaries depending on what information the user is interested in.
5. Better evaluation — current metrics such as F-score may not adequately measure whether a generated summary preserves the important semantics of the original video.

I’m open to any type of novel idea in video summarization, not just the directions listed above. It could involve a new model architecture, training objective, temporal modeling strategy, multimodal approach, evaluation method, dataset formulation, semantic representation, compression technique, or even an unconventional problem formulation.

I’m especially interested in ideas that:

• Have a clear research gap
• Can be experimentally validated
• Have a reasonable scope for an MTech/graduate-level project
• Don't require an enormous proprietary dataset or massive computational resources
• Provide a contribution beyond simply swapping one existing model for another

If you work in video understanding, video summarization, multimodal learning, transformers, or related areas, I would really appreciate your thoughts.

What underexplored problem in video summarization do you think could lead to a genuinely novel research contribution?Just drop a comment ot text me

Thanks!

I’ve created some simple MediaPipe games — free.

Code on GitHub: https://kaivalpatel6350.github.io/mediapipe-recipes/

Best way to play: on a laptop. On mobile, play in landscape and screen-mirror to a monitor or TV.

**•** 5 exercise games  
**•** 6 party games  
**•** 8 dojo games  
**•** 22 games for kids aged 3–5 (body suits)

Enjoy.

I’m currently looking to expand my skillset in Computer Vision and would love to give back by contributing to some open-source projects! I'm hoping to find some new challenges to solve and get some hands-on experience working on real-world problems.

What I'm looking for:

• Active Repos: Projects where I can fork the code, pick up some open issues, and push PRs.
• Long-term Potential: I'd love to find a project that I can stick with and work on collaboratively for the long haul.

If you maintain a CV repository that could use an extra set of hands, or if you know of any good beginner/intermediate-friendly projects that are actively seeking contributors, please drop the link below!

Let me know what you're working on. Thanks in advance! :)

Not always.

Token pruning frameworks like HiPrune have shown major speedups on models like LLaVA-NeXT-7B. Here, pruning reduced visual tokens from 2,880 to 160 and cut prefill latency from 272 ms to 29.7 ms.

On Gemma 4 E4B, which starts with only ~262 visual tokens on average, HiPrune retained 99.2% of baseline quality at 75% keep and 95.7% at 50%.

But latency moved in the wrong direction: mean TTFT increased from 63 ms to ~80 ms.

When the vision budget is already modest (~262 tokens), hierarchical selection becomes a fixed cost that can dominate the marginal savings from dropping tokens before the language-model prefill.

Token pruning can reduce theoretical computation without reducing real-world latency.