stuarteiffert/RNN-for-Human-Activity-Recognition-using-2D-Pose-Input
Activity Recognition from 2D pose using an LSTM RNN
This project helps researchers and engineers classify human actions like jumping or waving, as well as animal behaviors, using standard video camera footage. It takes in a series of 2D body joint positions (like a stick figure) extracted from video frames and outputs the likely activity being performed. This is useful for anyone studying movement patterns, human-robot interaction, or animal behavior.
296 stars. No commits in the last 6 months.
Use this if you need to automatically identify human or animal actions from standard 2D video, especially when working with limited datasets where simpler models are beneficial.
Not ideal if you require highly precise 3D motion analysis, need to classify very subtle movements, or are working with video where pose estimation is frequently inaccurate.
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Jun 02, 2021
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