observable-state-tree
AI-basketball-analysis
Our great sponsors
| observable-state-tree | AI-basketball-analysis | |
|---|---|---|
| 3 | 12 | |
| 7 | 922 | |
| - | - | |
| 1.8 | 0.0 | |
| over 3 years ago | 12 months ago | |
| TypeScript | Python | |
| - | 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.
observable-state-tree
-
Ask HN: Show me your Half Baked project
I have been working on an observable tree data structure which could form the basis of a state management library in typescript https://github.com/mfbx9da4/observable-state-tree
- Build an Observable State Tree
- Show HN: Observable State Tree
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
What are some alternatives?
learn-anything.xyz - Organize world's knowledge, explore connections and curate learning paths
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
ping-heatmap - A tool for displaying subsecond offset heatmaps of ICMP ping latency
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
RuntimeGeneratedFunctions.jl - Functions generated at runtime without world-age issues or overhead
veems - An open-source platform for online video.
json-tail
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
tinyjam - A radically simple, zero-configuration static site generator in JavaScript
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data