Human Activity Recognition

This repository performs Human Activity Recognition, where given a video, the developed framework recognizes the activity of the object per-frame using a pre-Trained 3D convolutional ResNet-34 model. The applications of it includes:-

• Automatically classifying/categorizing a dataset of videos on disk.
• Verifying that a food service worker has washed their hands after visiting the restroom or handling food that could cause cross-contamination (i.e,. chicken and salmonella).
• Monitoring bar/restaurant patrons and ensuring they are not over-served.

Requirements

• NumPy
• OpenCV
• collections

Usage

Data

• The pre-Trained 3D ResNet-34 model of the developed framework is trained on the Kinetics dataset.
• It consists of:-
  • 400 human activity recognition classes
  • At least 400 video clips per class (downloaded via YouTube)
  • A total of 300,000 videos
• The full list of classes the model can recognize can be seen here.

Training and Testing

• The pre-Trained weights of the 3D convolutional ResNet-34 model, trained on the Kinetics dataset, can be downloaded from here.
• To see the implementation of Human Activity Recognition without rolling prediction, check Human_Activity_Recognition.py. Moreover, to test this implementation on a test video, run the following command:- python Human_Activity_Recognition.py --model resnet-34_kinetics.onnx --classes Class_Labels/action_recognition_kinetics.txt --input example_activities.mp4
• To see the implementation of Human Activity Recognition with rolling prediction, check Human_Activity_Recognition_Deque.py. Besides, run the following command to check the performance of this implementation:- python Human_Activity_Recognition_Deque.py --model resnet-34_kinetics.onnx --classes Class_Labels/action_recognition_kinetics.txt --input example_activities.mp4

Results

Below are some results of developed model on test videos:-

Test Video 1	Test Video 2	Test Video 3

Test Video 4	Test Video 5