Abstract
The rapid evolution of deep learning (DL) models and the ever-increasing size of available datasets have raised the interest of the research community in the always important field of vision-based hand gesture recognition (VHGR), and delivered a wide range of applications, such as sign language understanding and human-computer interaction using cameras. Despite the large volume of research works in the field, a structured and complete survey on VHGR is still missing, leaving researchers to navigate through hundreds of papers in order to find the right combination of data, model, and approach for each task. The current survey aims to fill this gap by presenting a comprehensive overview of this aspect of computer vision. With a systematic research methodology that identifies the state-of-the-art works and a structured presentation of the various methods, datasets, and evaluation metrics, this review aims to constitute a useful guideline for researchers, helping them to choose the right strategy for delving into a certain VHGR task. Starting with the methodology used for study selection, literature retrieval, and the analytical framing, the survey identifies and organizes key VHGR approaches using a taxonomy-based format in various dimensions such as input modality and application domain. The core of the survey provides an in-depth analysis of state-of-the-art techniques across three primary VHGR tasks: static gesture recognition, isolated dynamic gestures and continuous gesture recognition. For each task, the architectural trends and learning strategies are listed. Additionally, the study reviews commonly used datasets - emphasizing on annotation schemes - and evaluates standard performance metrics. It concludes by identifying major challenges in VHGR, including both general computer vision issues and domain-specific obstacles, and outlines promising directions for future research.
Abstract (translated)
深度学习（DL）模型的快速演进和可用数据集规模的不断增大，使得基于视觉的手势识别（VHGR）这一长久以来重要的研究领域重新引起了科研社区的兴趣，并且催生了许多应用，例如手语理解和使用摄像头的人机交互。尽管该领域的研究成果众多，但至今仍缺乏系统性和全面性的综述文献，这导致研究人员需要在大量论文中寻找适用于特定任务的数据、模型和方法组合。当前的综述旨在填补这一空白，通过呈现计算机视觉领域中的这一方面进行全面概述。采用系统性研究方法识别最新成果，并以结构化的方式介绍各种方法、数据集及评估指标，该回顾力求成为科研人员的重要指南，帮助他们为探索特定的VHGR任务选择合适的战略。 本次综述从用于研究筛选、文献检索和分析框架的方法论开始，使用基于分类法的形式来识别并组织关键的VHGR方法，在输入模式和应用领域等多个维度进行分类。该综述的核心部分提供了对三个主要的VHGR任务——静态手势识别、孤立动态手势及连续手势识别的最新技术进行了深入分析，并列出了每项任务中的架构趋势和学习策略。此外，本研究还回顾了常用的数据库（特别强调标注方案），并评估标准性能指标。综述最后指出了VHGR领域的重大挑战，包括一般计算机视觉问题以及特定领域障碍，并概述了未来研究的潜在方向。
URL
https://arxiv.org/abs/2507.04465
