Abstract
Human Action Recognition (HAR) encompasses the task of monitoring human activities across various domains, including but not limited to medical, educational, entertainment, visual surveillance, video retrieval, and the identification of anomalous activities. Over the past decade, the field of HAR has witnessed substantial progress by leveraging Convolutional Neural Networks (CNNs) to effectively extract and comprehend intricate information, thereby enhancing the overall performance of HAR systems. Recently, the domain of computer vision has witnessed the emergence of Vision Transformers (ViTs) as a potent solution. The efficacy of transformer architecture has been validated beyond the confines of image analysis, extending their applicability to diverse video-related tasks. Notably, within this landscape, the research community has shown keen interest in HAR, acknowledging its manifold utility and widespread adoption across various domains. This article aims to present an encompassing survey that focuses on CNNs and the evolution of Recurrent Neural Networks (RNNs) to ViTs given their importance in the domain of HAR. By conducting a thorough examination of existing literature and exploring emerging trends, this study undertakes a critical analysis and synthesis of the accumulated knowledge in this field. Additionally, it investigates the ongoing efforts to develop hybrid approaches. Following this direction, this article presents a novel hybrid model that seeks to integrate the inherent strengths of CNNs and ViTs.
Abstract (translated)
人类动作识别（HAR）涵盖了在各种领域监测人类活动的任务，包括医疗、教育、娱乐、视觉监视、视频检索和异常活动识别。在过去的十年里，通过利用卷积神经网络（CNNs）有效地提取和理解复杂信息，HAR系统整体性能得到了显著提高。近年来，计算机视觉领域出现了 Vision Transformers（ViTs）作为一种强大的解决方案。ViTs 的效果已经超越了图像分析的范围，将它们的适用范围扩展到了各种视频相关任务。值得注意的是，在这个领域，研究社区对 HAR 表现出浓厚的兴趣，承认其在各个领域广泛应用和采用的事实。本文旨在呈现一篇全面调查，重点关注 CNNs 和 Recurrent Neural Networks（RNNs）到 ViTs 的演变，因为它们在 HAR 领域的重要性。通过深入研究现有文献并探索新兴趋势，本研究对这一领域的积累知识进行了批判性分析和综合。此外，还研究了当前为开发混合方法而进行的努力。沿着这个方向，本文呈现了一种新颖的混合模型，旨在整合 CNNs 和 ViTs 的固有优势。
URL
https://arxiv.org/abs/2407.06162
