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
Most implemented papers
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
xueyee/groupformer
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ICCV 2021
It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.
COMPOSER: Compositional Reasoning of Group Activity in Videos with Keypoint-Only Modality
Group Activity Recognition detects the activity collectively performed by a group of actors, which requires compositional reasoning of actors and objects.
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition
In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data.
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image
To that end, we propose a novel GAR technique for volleyball videos, DECOMPL, which consists of two complementary branches.
