aquamam
AI-basketball-analysis
| aquamam | AI-basketball-analysis | |
|---|---|---|
| 3 | 12 | |
| 1 | 923 | |
| - | - | |
| 3.6 | 0.0 | |
| 8 months ago | about 1 year ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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aquamam
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DeepVoxels: Learning Persistent 3D Feature Embeddings
This paper is near and dear to my heart because I saw the presentation at the first and only machine learning conference I've ever attended (thanks a lot, ICML, NeurIPS (x2), and ICLR!). It's a neural rendering approach that precedes NeRF, but you can see some similarities (even more so in the follow-up paper about "Scene Representation Networks"). Sitzmann and co-authors also published a paper about using sinusoidal activations in implicit representation models at NeurIPS 2020, the same conference where the "Fourier Features" paper (which has many of the same authors as the NeRF paper) was also presented. It's always interesting to me to see how ideas in science often pop-up at the same time from different researchers (e.g., attention).
- AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions
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[R] AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions
Code for the paper can be found here.
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?
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
Hierarchical-Localization - Visual localization made easy with hloc
baller2vec - A multi-entity Transformer for multi-agent spatiotemporal modeling.
go-live - 🗂️ go-live is an ultra-light server utility that serves files, HTML or anything else, over HTTP.
ailia-models - The collection of pre-trained, state-of-the-art AI models for ailia SDK
veems - An open-source platform for online video.
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
baller2vecplusplus - A look-ahead multi-entity Transformer for modeling coordinated agents.
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data