qrono
AI-basketball-analysis
| qrono | AI-basketball-analysis | |
|---|---|---|
| 1 | 12 | |
| 8 | 923 | |
| - | - | |
| 3.5 | 0.0 | |
| about 1 year ago | about 1 year ago | |
| Rust | Python | |
| GNU Affero General Public License v3.0 | 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.
qrono
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Ask HN: Show me your Half Baked project
https://qrono.net, https://github.com/c2nes/qrono
A work in progress, Qrono is a persistent, time-ordered queue server providing at-least-once delivery. The time-ordering can be used to schedule values to be delivered in the future, implement exponential backoff within a consumer, etc.
In addition to HTTP and gRPC interfaces, Qrono supports a RESP (https://redis.io/topics/protocol) interface allowing Redis tools (e.g. redis-cli) and clients to be used.
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?
Chronicle Queue - Micro second messaging that stores everything to disk
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
dupver - Deduplicating VCS for large binary files in Go
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
cnosdb - A cloud-native open source distributed time series database with high performance, high compression ratio and high availability. http://www.cnosdb.cloud
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
process-queue - Command-line task queue
veems - An open-source platform for online video.
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
invisible-ink - :secret: Gradually loading web fonts
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data