A Review of Machine Learning Methods Applied to Video Analysis Systems
Marios S. Pattichis Venkatesh Jatla Alvaro E. Ullao Cerna
The paper provides a survey of the development of machine-learning techniques
for video analysis. The survey provides a summary of the most popular deep
learning methods used for human activity recognition. We discuss how popular
architectures perform on standard datasets and highlight the differences from
real-life datasets dominated by multiple activities performed by multiple
participants over long periods. For real-life datasets, we describe the use of
low-parameter models (with 200X or 1,000X fewer parameters) that are trained to
detect a single activity after the relevant objects have been successfully
detected. Our survey then turns to a summary of machine learning methods that
are specifically developed for working with a small number of labeled video
samples. Our goal here is to describe modern techniques that are specifically
designed so as to minimize the amount of ground truth that is needed for
training and testing video analysis systems. We provide summaries of the
development of self-supervised learning, semi-supervised learning, active
learning, and zero-shot learning for applications in video analysis. For each
method, we provide representative examples.