Machine_Learning_and_Deep_Learning_models
Deep-Learning-Push-Up-Counter
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3.2 | 1.8 | |
over 3 years ago | almost 4 years ago | |
Jupyter Notebook | Jupyter Notebook | |
The Unlicense | MIT License |
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Machine_Learning_and_Deep_Learning_models
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AI 101 | Introduction to Artificial Intelligence
MuizAlvi / Machine_Learning_and_Deep_Learning_models
Deep-Learning-Push-Up-Counter
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Counting repetitions in a video
Hi ! I am currently working on a project which involves the recognition of tennis strokes in videos. I have already implemented the detection part using a pre-trained OpenPose model to find the body keypoints and an LSTM for the classification task. Now I am having some trouble counting those movements. I have seen various approaches, but I would like something lightweight, somehow related to this specific task. Here is a project which uses optical flows to detect patterns in the movements, but a tennis stroke is a more complex action and I don't know how to label the frames (swing, not swing maybe). Another solution would be to use the already computed keypoints to compute some angles, but I didn't find something related to tennis. Thank you!
What are some alternatives?
Face-Mask-Detection - Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras
SynthDet - SynthDet - An end-to-end object detection pipeline using synthetic data
AI101 - Repository containing guidance material for participants attending the AI101 Workshop
DenseDepth - High Quality Monocular Depth Estimation via Transfer Learning
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
YPDL-Build-a-movie-recommendation-engine-with-TensorFlow - In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.