Finding Headache Moments from YouTube Videos Using Weak Supervision
Abstract
Smart Monitoring of actions of elderly people have become more important these days
since they mostly live by themselves. Majority of them have some kind of illness and
require monitoring in their daily life. Computer Vision and Deep Learning can play a vital
role in monitoring critical actions of the elderly people and the detected information can
be very useful for their primary doctors and their kith and kin to care of them. To build a
strong deep learning model that can detect ’headache’ moments from videos, the biggest
bottleneck is huge amount of labeled training data. The reality is that hand labeled datasets
are expensive and may take many months or in some cases years to create. The practical
deployment of deep learning is hindered by the cost and intractability of hand labeling
such datasets. This bottleneck has led to many machine learning systems use some form
of weak supervision. The present study aims to use multiple weak supervision sources
such as a pretrained deep learning model and several handcrafted heuristic rules; integrate
and model them using Snorkel [1] which helps to programmatically build training datasets
without manual labeling, from YouTube videos. A False Positive Rate (FPR) of 0.08% and
False Negative Rate of 0.01% were achieved using the DNN model.This research study
has shown that the accuracy of the combined weak supervision model is superior than
the single pretrained model for programmatically building training dataset consisting of
headache moments.
Subject
weak supervisionCitation
Jayagopal, Jagadish Kumaran (2021). Finding Headache Moments from YouTube Videos Using Weak Supervision. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /195104.