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.

在计算机视觉领域，团体活动识别是一个热门话题。通过对团体关系进行活动识别，在视频分析、监控、自动驾驶和理解社交活动等各种场景中具有实际意义。本文综述了团体活动识别技术的现有研究进展，重点关注全局交互性和活动。首先，全面回顾了相关文献和各种团体活动识别方法，从传统方法到基于空间结构、描述符、非深度学习、分层循环神经网络（HRNN）、关系模型和注意机制的最新方法。其次，针对每个模块提出了关系网络和关系架构。第三，调查了团体活动识别的方法，并将其性能与最先进的技术进行了比较。总结了现有的挑战，并为新手提供了全面的指导，以便理解团体活动识别。此外，还回顾了团体活动识别中的新视角，探索了新的方向和可能性。