Group Activity Recognition
14 papers with code • 2 benchmarks • 2 datasets
Group Activity Recognition is a subset of human activity recognition problem which focuses on the collective behavior of a group of people, resulted from the individual actions of the persons and their interactions. Collective activity recognition is a basic task for automatic human behavior analysis in many areas like surveillance or sports videos.
Source: A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition
Benchmarks
These leaderboards are used to track progress in Group Activity Recognition
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Volleyball
Collective Activity
Latest papers with no code
Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition
Social group activity recognition is a challenging task extended from group activity recognition, where social groups must be recognized with their activities and group members.
Group Activity Recognition using Unreliable Tracked Pose
Group activity recognition in video is a complex task due to the need for a model to recognise the actions of all individuals in the video and their complex interactions.
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space
In this paper, we focus on a group activity by which a group of users perform a collaborative task without user identification and propose an efficient group activity recognition scheme which extracts causality patterns from pervasive sensor event sequences generated by a group of users to support as good recognition accuracy as the state-of-the-art graphical model.
REACT: Recognize Every Action Everywhere All At Once
Group Activity Recognition (GAR) is a fundamental problem in computer vision, with diverse applications in sports video analysis, video surveillance, and social scene understanding.
Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives
This work examines the current progress in technology for recognizing group activities, with a specific focus on global interactivity and activities.
Learning from Synthetic Human Group Activities
The study of complex human interactions and group activities has become a focal point in human-centric computer vision.
Group Activity Recognition via Dynamic Composition and Interaction
Group composition tells us the location of people and their relations inside the group, while interaction reflects the relation between humans and objects outside the group.
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition
This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data.
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition
MLP-T is used to model the temporal relation between different frames for each actor.
Knowledge Augmented Relation Inference for Group Activity Recognition
Specifically, the framework consists of a Visual Representation Module to extract individual appearance features, a Knowledge Augmented Semantic Relation Module explore semantic representations of individual actions, and a Knowledge-Semantic-Visual Interaction Module aims to integrate visual and semantic information by the knowledge.
