Hello Nisha, Thanks for sharing your work. It's great. I am working on the same topic of Driver drowsiness detection.
My dataset has 25fps and labeling of drowsy or alert is done in every 2 minutes.I want to stick to a window size of 25.
Which of the below 2 methods is a better way of sampling data??
Should I consider the sequential frames from 119sec-120sec for classification ?? OR
Should I consider every 120th frame(every 5th second) for classifying drowsy or alert??
I see that your input has a window size of 26. So where these frames collected sequentially or after regular intervals of time?
I want to firstly have a try of this drowsy detection model with a live video from camera, but as I see from extract_features.py, the model "/home/tejas/Desktop/Course-Project-CV/output_graph.pb" which used to extract CNN features is a local path, can you share it to us? And it's much more better to give instructions that how to train this CNN models.