AI-basketball-analysis
live_data
Our great sponsors
| AI-basketball-analysis | live_data | |
|---|---|---|
| 12 | 2 | |
| 923 | 9 | |
| - | - | |
| 0.0 | 0.0 | |
| 12 months ago | about 1 year ago | |
| Python | Elixir | |
| GNU General Public License v3.0 or later | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
AI-basketball-analysis
-
[P] Basketball Shots Detection and Shooting Pose Analysis (Open Source)
Source code: https://github.com/chonyy/AI-basketball-analysis
- Show HN: Visualizing Basketball Trajectory and Analyzing Shooting Pose
- Automatically Overlaying Baseball Pitch Motion and Trajectory in Realtime (Open Source)
- Show HN: AI Basketball Analysis Web App and API
- Show HN: Visualize and Analyze Basketball Shots and Shooting Pose with ML
-
Ask HN: Show me your Half Baked project
I built an app to visualize and analyze basketball shots and shooting pose with machine learning.
https://github.com/chonyy/AI-basketball-analysis
The result is pretty nice. However, the only problem is the slow inference speed. I'm now refactoring the project structure and changing the model to a much faster YOLO model.
-
Show HN: Automatic Baseball Pitching Motion and Trajectory Overlay in Realtime
Thanks for asking! This is not a noob question.
I would say that the similar workflow could be applied to any ball-related sports. The object detection and the tracking algorithm is basically the same. Then, you could add any sport-specific feature!
For example, I have used a similar method to build AI Basketball Analysis.
https://github.com/chonyy/AI-basketball-analysis
- Show HN: AI Basketball Analysis in Realtime
- Show HN: AI Basketball Visualization
live_data
-
The future of web software is HTML over WebSockets
> Let the browser maintain a DOM tree that templates over a JSON object, then reactive-ly update and it's an amazing experience
Totally agreed. I even pulled off an Elixir library as a POC to show this concept, here's an example project: https://github.com/surferseo/live_data/tree/master/examples/... (most relevant part of API is here: https://github.com/surferseo/live_data/blob/master/examples/... and here: https://github.com/surferseo/live_data/blob/master/examples/...)
-
Ask HN: Show me your Half Baked project
https://github.com/surferseo/live_data/tree/master/examples/...
What are some alternatives?
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
beaker - An experimental peer-to-peer Web browser
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
pastty - Copy and paste across devices
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
tuna-lang
veems - An open-source platform for online video.
abs_cd - CI/CD for the Arch build system with webinterface.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
pglet - Pglet - build internal web apps quickly in the language you already know!
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
Phoenix - Peace of mind from prototype to production