Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis

TL;DR

This paper reviews recent advancements in deep neural networks for analyzing spatiotemporal features in videos, highlighting structural designs, challenges, and datasets in video understanding and action recognition.

Contribution

It provides a comprehensive review of recent deep learning models, challenges, and datasets in the field of video understanding and spatiotemporal analysis.

Findings

Deep neural networks have shown promising results in feature extraction.

Various model architectures are tailored for spatiotemporal video analysis.

Multiple datasets are used for benchmarking video understanding tasks.

Abstract

It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to dominate the digital landscape. These algorithms will extract and classify related features from the video and will use them to describe the events and objects in the video. Deep neural networks have displayed encouraging outcomes in the realm of feature extraction and video description. This paper will explore the spatiotemporal features found in videos and recent advancements in deep neural networks in video understanding. We will review some of the main trends in video understanding models and their structural design, the main problems, and some offered solutions in this topic. We will also review and compare significant video understanding and action recognition datasets.