Group Activity Recognition in Visual Data: A Retrospective Analysis of Recent Advancements

Abstract

Human-activity recognition has gained significant attention recently within the computer vision and machine learning community, due to its applications in diverse fields such as health, entertainment, visual surveillance and sports analytics. An important sub-category of human-activity recognition is group activity recognition (GAR) where a group of individuals is involved in an activity. The main challenge in such recognition tasks is to learn the relationship between a group of individuals in a scene and its evolution over time. Recently, many techniques based on deep networks and graphical models have been proposed for group activity recognition. In this paper, we critically analyze the state-of-the-art (SOTA) techniques for group activity recognition. We propose a new taxonomy for categorizing the SOTA techniques conducted in the field of group activity recognition and divide the existing literature into different subcategories. We also identify the available datasets and the existing research challenges for GAR.