English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The paper provides a survey of the development of machine-learning techniquesfor video analysis. The survey provides a summary of the most popular deeplearning methods used for human activity recognition. We discuss how populararchitectures perform on standard datasets and highlight the differences fromreal-life datasets dominated by multiple activities performed by multipleparticipants over long periods. For real-life datasets, we describe the use oflow-parameter models (with 200X or 1,000X fewer parameters) that are trained todetect a single activity after the relevant objects have been successfullydetected. Our survey then turns to a summary of machine learning methods thatare specifically developed for working with a small number of labeled videosamples. Our goal here is to describe modern techniques that are specificallydesigned so as to minimize the amount of ground truth that is needed fortraining and testing video analysis systems. We provide summaries of thedevelopment of self-supervised learning, semi-supervised learning, activelearning, and zero-shot learning for applications in video analysis. For eachmethod, we provide representative examples.

A Review of Machine Learning Methods Applied to Video Analysis Systems