a-brief-history-of-yesterday
AI-basketball-analysis
| a-brief-history-of-yesterday | AI-basketball-analysis | |
|---|---|---|
| 1 | 12 | |
| 0 | 923 | |
| - | - | |
| 1.8 | 0.0 | |
| over 3 years ago | about 1 year ago | |
| Vue | 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.
a-brief-history-of-yesterday
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Ask HN: Show me your Half Baked project
Yes, the 'Health and environment' section is quite overwhelming, mostly due to COVID-19. That's why I close each section initially, this way you open only the sections that interest you.
I modify the tree from Wikipedia slightly to get a less cluttered overview.[1]
[1] https://github.com/RubenVanEldik/a-brief-history-of-yesterda...
AI-basketball-analysis
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[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
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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.
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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?
thgtoa - The Hitchhiker’s Guide to Online Anonymity
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
veems - An open-source platform for online video.
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
hof - Framework that joins data models, schemas, code generation, and a task engine. Language and technology agnostic.
live_data
human-detection - yolov3 tensorflow object detection and report human movements in persian
fiftyone - The open-source tool for building high-quality datasets and computer vision models
espn-api - ESPN Fantasy API! (Football, Basketball)