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arXiv · 2305.15692

Deep Neural Networks in Video Human Action Recognition: A Review

Zihan Wang·Yang Yang·Zhi Liu·Yifan Zheng
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Abstract

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.

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Zihan Wang, Yang Yang, Zhi Liu, Yifan Zheng. 2023-05-25. Deep Neural Networks in Video Human Action Recognition: A Review. https://arxiv.org/abs/2305.15692

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