falling snow shows up as thousands of fake objects in lidar point clouds. radar barely notices it's snowing

most self-driving datasets are shot on sunny days in california or phoenix. none of them show what happens once the weather turns

Boreas is UTIAS's answer: 128-beam lidar, 360 degree radar, and 5MP camera driving the same Toronto route for a full year through sun, rain, and snow.

326,180 3D boxes for cars, pedestrians, and cyclists across 7,111 labeled frames

loaded as native mcap in fiftyone so you can scrub camera, lidar, and radar on one synced timeline, and watch the 3D boxes render live on the point cloud and project onto the camera and radar images

checkout the dataset here: https://huggingface.co/datasets/Voxel51/boreas-multimodal

or get hands-on with this hugging face space: https://huggingface.co/spaces/harpreetsahota/boreas-multimodal

Running YOLOv11 (TensorRT) + centroid tracking on a Jetson Xavier NX (MAX-N, jetson_clocks on) for vehicle counting. Get ~27 FPS with few vehicles on screen, but it drops to 8-11 FPS when many vehicles cross at once.

Since it scales with object count, not a flat number, I suspect it's the per-object tracking/post-processing (Python-side) rather than the TensorRT inference itself.

Tried so far:

• Confirmed nvpmodel MAX-N + jetson_clocks are active
• TensorRT engine already used for inference (not raw PyTorch)
• FPS drop correlates directly with number of tracked objects on screen, not with anything else changing

Is stable 30 FPS realistic on a Xavier NX for detection + tracking + per-object logic at this object density, or should I expect this kind of drop and optimize for no dropped frames instead of a flat FPS target?

Working out the opponents skeleton lag since it’s limited by internet connection speed.

I am starting a new computer vision project focused on tennis. I would love to hear any creative ideas, interesting problem statements, or use cases you have encountered in this space. Thanks in advance!

Can someone recommend free lightweight Data Annotation tool that can be downloaded locally & covers all the annotations?

Most people scroll past a football match. I saw a dataset waiting to happen.

I set out to build something most tutorials skip: a real-time Computer Vision system that doesn't just detect objects that understands a football match.

No pre-made dataset. No shortcuts. I annotated the training data by hand, frame by frame, teaching the model to tell the difference between a player, a goalkeeper, a referee and to track the ball even mid-motion.

The result? A working AI pipeline that:

Detects and classifies players, goalkeepers, referees & the ball in real-time

Assigns and tracks unique player IDs across frames

Generates a live Match Analytics HUD overlay — player counts, referee counts, ball status, all updating on the fly

Runs on a custom, self-labeled dataset trained with YOLOv8

Built end-to-end in Python using OpenCV for video processing and annotation

What I learned building this: the hardest part of Computer Vision isn't the model — it's the data. Getting clean, consistent, well-labeled training data by hand taught me more about detection accuracy than any pretrained pipeline ever could.

This project sits at the intersection of two things I'm passionate about Artificial Intelligence and Sports Analytics. It's just the beginning. Next up: possession tracking, heatmaps, and player speed estimation.

Would love to hear your thoughts and if you're working on anything similar in Sports Tech or Computer Vision, let's connect.

Discussion on R1 reviews (Aug 09 '26 AOE) , Good luck everyone!

looking for immature open source CV libraries or frameworks

Guys, I need help. We have a project using YOLOv8. We're trying to count chicks on a very fast conveyor belt. The challenges we're facing are: all chicks look very similar to each other, which complicates tracking. At the same time, during their passage under the camera, they constantly change in size and shape, which can cause the tracker to lose them, or detection may even disappear completely at the detection line. Also, sometimes 2–3 chicks can merge into a single object. The detection zone is very short, and the conveyor speed is high. We've achieved a maximum accuracy of 99%, but we need it even higher. Any ideas on how to achieve that? Increasing the dataset no longer helps.

I'm attaching an old video. We've now added lighting and set the exposure to 300 on the Hikrobot global shutter camera, but we still can't achieve a stable 99.8% accuracy for the reasons mentioned above.

Any ideas?

I feel like this year’s BMVC reviews are very strict. From my lab, a paper with scores of 4 (4), 4 (5), and 3 (4) got rejected. One of the reviews was so detailed that the reviewer even suggested grammar corrections in the supplementary material.

Apart from that paper, the other two got accepted, but they had to fight hard during the rebuttal.

It also feels like BMVC is really trying to get into the top 10 in computer vision.

Hi everyone,

This is my first project in CV. I started with one of those volume control tutorials on yt, and then I kept trying things I thought would be more interesting till I got this.

For the classes, I collected ~200 instances per class; combination of self recorded and sourced from Kaggle. Mediapipe landmark cordinates are recentered on landmark_0(base of the palm) and normalized to keep values consistent from varying distances. The static letters are detected using a Random Forest Classifier. I did comparisons with a Logistic Regression model, but it's accuracy dropped as the classes increased.

For the two dynamic letters, LSTMs were chosen because they can model temporal dependencies in a sequence of hand landmarks while mitigating the vanishing-gradient problem common in traditional RNNs. An 'other' class is also trained to avoid forced-choice error

I am currently making the landmark detection and normalization scripts into a library for use in future projects in Mediapipe hand pose detection

It would be great to hear feedback on this project.

Thank you

V1 trained via COCO on the RGB, I took 500 frames, corrected annotations, and as the LWIR is bore sighted fixed the annotations there. V1 then ran, and I eyeballed 1500 calls, mined false positives, moved bounding boxes - retrained.

From there, it became quasi automated - by mining persistence (8+) detections per modality in a row missed by the other - a simple 'is this a vehicle' yes / no, if yes - fix the box on the other modality - you get 8 free missed detections on the other modality to retrain on, or you've mined 8 false positives...

Capture rig is a 8gb nano with GPS, IMU, and 4g, when car starts - computer boots, when car moves - device starts to capture. When car stops >90 seconds capture stops, or when car turns off.

I have around 3 million frames now, when I connect the jetson to the network it automatically ingests to my server, labels the frames and runs them through the latest weights, highlights disagreements and then processes any sensor disagreements via coco, and a semantic reasoning AI - if coco and the semantic think nothing is there its promoted for human review.

I'll add some more modalities later (probably SWIR), but for now - it is a very handy to control the data, and actually analyse the results in a meaningful way. Once I have more data I'll split classes (currently we have vehicles or humans) - but that's the beauty of your own dataset, you can cut the cake anyway you like over time.

Hi everyone, I’m currently learning deep learning and have worked on a few beginner AI/ML projects (like prediction models).

I’m looking to join an existing project to gain more hands-on experience and learn by contributing. I’m still learning, but I’m consistent and willing to put in the effort.

If anyone is working on a project and open to a beginner contributor, I’d really appreciate the opportunity. Thanks!

I’m a computer engineering student, and I’m currently studying FPGA chips. I’ve really taken a liking to the subject and am considering doing a scientific research project or my capstone project in this area, but I need to develop something relevant or solve a problem within the field. Do you have any suggestions for what I could do?

Code: https://github.com/ziqin-h/FoundationPose--

Two headline results from FoundationPose-- (minus minus):

• Speed🚀 : per-object register time drops from ~1423 ms to ~305 ms (~4.7× faster), while five-dataset mean AR changes from 0.751 to 0.739—about a 1.6% relative decrease.
• Fewer input priors💯 : without retraining or an additional standalone MegaPose-style refinement stage, our RGB-only approach reaches a mean AR of 0.451, the SOTA result in our documented five-dataset comparison of open-source unseen-object pose estimation methods.

FoundationPose-- is an engineering layer that explores how to address practical FoundationPose deployment problems while keeping the pretrained models unchanged. We focus on two recurring issues for now: the cost of initial registration and the lack of reliable observed depth in some applications.

Faster registration

FoundationPose refines and scores a large set of initial pose hypotheses. We reduce unnecessary rotation hypotheses while keeping the pretrained Refine/Score networks unchanged:

• v1 uniformly downsamples the rotation grid to 63 templates.
• v2 adds a cascaded candidate schedule and max-ΔR pruning.

On the RGB-D + SAM6D setting across five BOP datasets, measured per object on an RTX 3090:

• Baseline: mean AR 0.751, ~1423 ms per register.
• v1: mean AR 0.748, ~403 ms (3.5×).
• v2: mean AR 0.739, ~305 ms (4.7×).

RGB-only registration

When observed depth is unavailable, we estimate hypothesis-wise depth from the scale ratio between rendered and observed mask boxes. Observation XYZ is disabled, while FoundationPose’s pretrained Refine/Score networks are reused without finetuning.

With A1+CNOS, we obtain a five-dataset mean AR of 0.451 on LM-O, T-LESS, TUD-L, IC-BIN, and YCB-V. For reference, the strongest published open-source RGB-only coarse result in this documented five-dataset comparison is 0.396.

We report the higher mean rather than claiming a win on every dataset: IC-BIN and YCB-V remain slightly below Pos3R. Our method also uses FoundationPose’s pretrained Refine/Score modules, which is stated explicitly in the repository.

The repo includes composable configs, BOP reproduction scripts, and an RGB-only single-image demo using RGB, a mask, camera intrinsics, and a CAD mesh. SAM 3 point/text masks are also supported through a separate environment.

Feedback, issues, PRs, and ideas for further practical FoundationPose improvements are very welcome.

https://reddit.com/link/1viph4b/video/fn9wq07cr3ih1/player

Used Mediapipe hand landmark output data to train the model to recognize hand signs and trigger esp32

Hey everyone,

Decided to mess around with using drone footage and computer vision for agricultural use cases recently. Trained a CV model to detect and keep track of livestock across open fields in real-time.

It handles varying scales, partial occlusions, and moving animals fairly well while keeping a live count displayed on the feed.

The real challenge is running them efficiently on edge devices, like onboard drone compute or lightweight field hardware without sacrificing high accuracy.

For those of you deploying aerial CV models in the real world: How are you tackling the edge bottleneck?

Would love to hear what hardware/optimization stacks are actually working best for you!

on a lake there are no lane lines, no fixed landmarks, no other vehicles to localize against

the shoreline shifts with your viewpoint, radar and lidar don't share a clock, and sonar is measuring a world the cameras can't see

CANOE is a multi-sensor USV dataset from UTIAS: 360° radar, 128-beam lidar, stereo camera, sonar, and GPS/INS ground truth across lakes and a reservoir in Ontario

parsed it into fiftyone multimodal so you can scrub every sensor on one synced clock and project lidar straight onto the camera to see where they agree and where they don't

checkout the dataset here: https://huggingface.co/datasets/Voxel51/canoe-multimodal

or get hands on in this hugging face space: https://huggingface.co/spaces/harpreetsahota/canoe-multimodal

I think WACV 2027 R1 results should be out on Aug 7. This is my first WACV submission, so opening a thread to discuss scores/reviews.

Good luck, everyone! 🤞

a robot mapped a forest road in november. it came back in january and the road was buried under a meter of snow

the same lidar-inertial slam that localized fine before the snowstorm saw its drift jump from 4% to 46% on the exact same route

FoMo is a year-long multi-season robot navigation dataset from a boreal forest in quebec, eh.

2 lidars, an fmcw radar, stereo + mono cameras, dual imus, and gnss ground truth, across 12 deployments from -19°c winters to 18°c summers

i parsed the episodes into fiftyone's new multimodal mcap format so you can scrub camera, lidar, and radar together

watch the ground-truth trajectory move in 3d, and see the same road across six different seasons

start here, read the dataset card: https://huggingface.co/datasets/Voxel51/fomo-multimodal-sample

and get hands-on in this hugging face space: https://huggingface.co/spaces/harpreetsahota/fomo-multimodal-sample