  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.
