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Human-Activity-Recognition

This project uses computer vision techniques and deep learning to recognize human activities in real-time. The application captures video frames from a webcam, and using a pre-trained convolutional neural network model, it predicts the activity being performed. The model has been trained on the Kinetics dataset, which contains over 400 human action classes.

Usage

To use this application, simply run the human_activity_recognition.py script. Make sure you have the necessary dependencies installed, which can be found in the requirements.txt file. You will also need to download the pre-trained model and activity class labels, which can be found in the models folder.

When you run the script, your webcam will turn on and begin capturing frames. The application will predict the activity being performed in real-time, and display it on the screen.

Dependencies

This project requires the following dependencies:

  • Python 3.6 or higher
  • OpenCV 4.5.3 or higher
  • NumPy 1.20.3 or higher
  • Onnxruntime 1.9.0 or higher

You can install all dependencies by running pip install -r requirements.txt.

Setup :

  • I have added a video example for testing in test directory
  • If you want to test your own video file be sure to add it in test folder
  • Now, inside recognise_human_activity.py constructor set instance variable VIDEO_PATH to you file path.
  • Otherwise, if you want test the model on using web-camera live video just set self.VIDEO_PATH = None
  • Once your setup is done run the following to execute code:
  • python recognise_human_activity.py

Acknowledgments

This project was inspired by the work done in this blog post by Adrian Rosebrock at PyImageSearch. The pre-trained model was trained by the authors of the Kinetics dataset, and can be found on their GitHub repository.