dr_blueberry
Joined 5 months ago
Joined 5 months ago
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 it would be great to get quantitative feedback.
There are definitely things to improve, but overall I’m encouraged by this first demo.
Models used:
- VLM Run’s Orion for segmentation (https://www.vlm.run/)
- 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/
1. Crop the segment when adding rice to the rice cooker + analyze a frame: https://chat.vlm.run/c/40b5edfb-6d15-47e8-bedd-c77b1ed92496
2. Extract 16 keyframes in a 4x4 grid + detect the water bottle: https://chat.vlm.run/c/a8542517-0021-4013-9663-a9f41f286e4c
3. Extract the frame at 0:03 + segment the lettuce + generate a 3D reconstruction: https://chat.vlm.run/c/494c7c65-4aa1-442f-a78a-bfd1409c784d
(I work at VLM Run).
So far, they are based on ViTPose+ Large pose estimation in the context of different sports.
My demos include:
- Comparing dancers' sync performing the same choreography and computing a sync score
- Visualizing a runner's ankle path while running, as well as the average knee shape upon foot strike
- Counting chin-ups reps and measuring each rep duration
The code is open-source on GitHub:
https://github.com/jeremyipark/vision-demos