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Dynamic-Gesture-Recognition

This repository contains a project for a master degree research. The aim of the research is to create an AI based system of spatio-temporal user gesture recognition. The 3D-CNN Model used in this case is trained on a custom dataset consisting of multiple videos collected from volunteers

Technology Stack:

  • Python 3.8
  • TensorFlow 2.13.0
  • Keras 2.13.1
  • OpenCV 4.8.1
  • MediaPipe 0.10.5

Modules description:

  • model.py: contains 3D-CNN model built with Keras API

  • train-app.py: Main module for training the 3D-CNN model, saving training results and data visualisation

  • test-app.py: Module for testing the trained module in realtime gesture classification

  • video-to-frames.py: Video processing module for extracting frames form video

  • skeleton-to-image.py: Frame processing module for applying skeleton to image.

  • augment-data.py: Horizontal flip of a video in order to augment dataset

  • filter-one: Outlier detector which excludes skeletons which lack main landmarks

  • filter-two: Outlier detector, excludes gestures which are out of expected range of performance