Abstract
Human Action Recognition (HAR) is a challenging domain in computer vision, involving recognizing complex patterns by analyzing the spatiotemporal dynamics of individuals' movements in videos. These patterns arise in sequential data, such as video frames, which are often essential to accurately distinguish actions that would be ambiguous in a single image. HAR has garnered considerable interest due to its broad applicability, ranging from robotics and surveillance systems to sports motion analysis, healthcare, and the burgeoning field of autonomous vehicles. While several taxonomies have been proposed to categorize HAR approaches in surveys, they often overlook hybrid methodologies and fail to demonstrate how different models incorporate various architectures and modalities. In this comprehensive survey, we present the novel SMART-Vision taxonomy, which illustrates how innovations in deep learning for HAR complement one another, leading to hybrid approaches beyond traditional categories. Our survey provides a clear roadmap from foundational HAR works to current state-of-the-art systems, highlighting emerging research directions and addressing unresolved challenges in discussion sections for architectures within the HAR domain. We provide details of the research datasets that various approaches used to measure and compare goodness HAR approaches. We also explore the rapidly emerging field of Open-HAR systems, which challenges HAR systems by presenting samples from unknown, novel classes during test time.
Abstract (translated)
人类动作识别（HAR）是计算机视觉领域中的一个挑战性课题，涉及通过分析视频中个体运动的时空动态来识别复杂的模式。这些模式出现在序列数据中，如视频帧，并且通常对于准确地区分在单个图像中会显得模糊的动作至关重要。由于其广泛的应用性——从机器人技术和监控系统到体育动作分析、医疗保健以及新兴的自动驾驶汽车领域——HAR引起了极大的兴趣。 尽管已有多份文献提出了分类HAR方法的不同体系，但它们往往忽视了混合的方法，并未能展示不同模型如何融合各种架构和模态的特点。在这篇全面的综述中，我们介绍了SMART-Vision分类法这一新颖的概念，该分类法展示了深度学习在HAR领域的创新如何相互补充，从而催生出超越传统类别的混合方法。我们的调查提供了一个从基础HAR工作到当前最先进的系统的清晰路线图，并强调了新兴的研究方向，同时在架构部分的讨论中解决未决挑战。 我们详细介绍了各种方法使用的研究数据集及其用来衡量和比较HAR方法优劣的具体细节。此外，我们也探讨了迅速发展的开放HAR系统领域，这些系统通过测试时展示来自未知、新颖类别的样本对HAR系统提出了新的挑战。
URL
https://arxiv.org/abs/2501.13066
