Literature Review of Action Recognition in the Wild

TL;DR

This paper provides an in-depth review of action recognition in untrimmed videos, highlighting the evolution from handcrafted features to advanced deep learning methods.

Contribution

It offers a comprehensive survey of existing research, comparing traditional and deep learning approaches for action recognition in unconstrained environments.

Findings

Deep learning techniques outperform handcrafted features in accuracy.

End-to-end models have become more prevalent in recent studies.

Challenges remain in recognizing actions in complex, untrimmed videos.

Abstract

The literature review presented below on Action Recognition in the wild is the in-depth study of Research Papers. Action Recognition problem in the untrimmed videos is a challenging task and most of the papers have tackled this problem using hand-crafted features with shallow learning techniques and sophisticated end-to-end deep learning techniques.