Group activity recognition is a hot topic in computer vision. Recognizing activities through group relationships plays a vital role in group activity recognition. It holds practical implications in various scenarios, such as video analysis, surveillance, automatic driving, and understanding social activities. The model's key capabilities encompass efficiently modeling hierarchical relationships within a scene and accurately extracting distinctive spatiotemporal features from groups. Given this technology's extensive applicability, identifying group activities has garnered significant research attention. This work examines the current progress in technology for recognizing group activities, with a specific focus on global interactivity and activities. Firstly, we comprehensively review the pertinent literature and various group activity recognition approaches, from traditional methodologies to the latest methods based on spatial structure, descriptors, non-deep learning, hierarchical recurrent neural networks (HRNN), relationship models, and attention mechanisms. Subsequently, we present the relational network and relational architectures for each module. Thirdly, we investigate methods for recognizing group activity and compare their performance with state-of-the-art technologies. We summarize the existing challenges and provide comprehensive guidance for newcomers to understand group activity recognition. Furthermore, we review emerging perspectives in group activity recognition to explore new directions and possibilities.

翻译：群体活动识别是计算机视觉领域的热点课题。通过群体关系识别活动在群体活动识别中发挥着关键作用，在视频分析、监控、自动驾驶及社交活动理解等多种场景中具有重要应用价值。该模型的核心能力包括高效建模场景内的层次化关系，以及准确提取群体独特的时空特征。鉴于该技术的广泛适用性，群体活动识别已成为备受关注的研究方向。本文系统梳理了群体活动识别技术的当前进展，重点关注全局交互性与活动性。首先，我们全面回顾了相关文献及各类群体活动识别方法——从传统方法到基于空间结构、描述符、非深度学习、层次循环神经网络、关系模型及注意力机制的最新方法。其次，针对每个模块提出了关系网络及其架构。再次，我们研究了群体活动识别方法，并与前沿技术进行了性能对比。我们总结了现有挑战，为新手理解群体活动识别提供了全面指导。此外，我们综述了群体活动识别领域的新兴观点，以探索新的方向与可能性。