/fall-detection-two-stream-cnn

Real-time fall detection using two-stream convolutional neural net (CNN) with Motion History Image (MHI)

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

Fall Detection Two Stream CNN

This repository is forked on vietdzung/fall-detection-two-stream-cnn

Real-time fall detection using two-stream convolutional neural net (CNN) with Motion History Image (MHI)

This repository contains code for a real-time fall detection model using two-stream CNN. The optical flow stream is replaced with Motion History Image (MHI) to allow for real-time inference. The utils.py file contains utility code for generating the data, the train_model.py file creates and trains the model, and the fall_detection.py file contains code that runs the model with the weight in the weights folder either on the FDD dataset, a video, or your webcam. More detailed description of the model architecture, performance, as well as demo footage/pictures to come in the near future. Achieved fairly good cross-validated error rate on a subset of data generated. Currently working on acquiring more data and refining data generation technique.

Test the demo

First of run it

First of all, make sure that you has been installed follows

  • OpenCV ( include OpenCV-contrib )
  • Keras
  • Numpy ( vision = 1.15 )
  • TensorFlow

Run it

python fall_detection,py

Train your own dataset

Firstly,