Currently, video behavior recognition is one of the most foundational tasks
of computer vision. The 2D neural networks of deep learning are built for
recognizing pixel-level information such as images with RGB, RGB-D, or optical
flow formats, with the current increasingly wide usage of surveillance video
and more tasks related to human action recognition. There are increasing tasks
requiring temporal information for frames dependency analysis. The researchers
have widely studied video-based recognition rather than
image-based(pixel-based) only to extract more informative elements from
geometry tasks. Our current related research addresses multiple novel proposed
research works and compares their advantages and disadvantages between the
derived deep learning frameworks rather than machine learning frameworks. The
comparison happened between existing frameworks and datasets, which are video
format data only. Due to the specific properties of human actions and the
increasingly wide usage of deep neural networks, we collected all research
works within the last three years between 2020 to 2022. In our article, the
performance of deep neural networks surpassed most of the techniques in the
feature learning and extraction tasks, especially video action recognition.

本篇论文研究了基于视频的人体行为识别任务，通过比较现有的深度学习框架（尤其是视频动作识别的表现）和机器学习框架，提出了多项新颖的研究方案。