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Recent advances in video-based human action recognition using deep learning: A review

机译：使用深度学习的基于视频的人体动作识别的最新进展：综述

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摘要

Video-based human action recognition has become one of the most popular research areas in the field of computer vision and pattern recognition in recent years. It has a wide variety of applications such as surveillance, robotics, health care, video searching and human-computer interaction. There are many challenges involved in human action recognition in videos, such as cluttered backgrounds, occlusions, viewpoint variation, execution rate, and camera motion. A large number of techniques have been proposed to address the challenges over the decades. Three different types of datasets namely, single viewpoint, multiple viewpoint and RGB-depth videos, are used for research. This paper presents a review of various state-of-the-art deep learning-based techniques proposed for human action recognition on the three types of datasets. In light of the growing popularity and the recent developments in video-based human action recognition, this review imparts details of current trends and potential directions for future work to assist researchers.
机译：基于视频的人类行动认可已成为近年来计算机视觉和模式识别领域最受欢迎的研究领域之一。它具有各种各样的应用，如监控，机器人，保健，视频搜索和人机互动。视频中的人类行动认可涉及许多挑战，例如杂乱的背景，闭塞，观点变化，执行速率和相机运动。已经提出了大量技术来解决几十年来的挑战。三种不同类型的数据集即，单个视点，多视点和RGB深度视频用于研究。本文提出了对三种类型的数据集上的人类行动认可提出了各种最先进的深层学习技术的审查。鉴于越来越受欢迎和基于视频的人类行动认可的最新发展，促进了当前趋势和潜在指示的细节，以协助研究人员。

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