Towards More Practical Group Activity Detection: A New Benchmark and Model

• URL: http://arxiv.org/abs/2312.02878v2
• Date: Thu, 25 Jul 2024 15:20:48 GMT
• Title: Towards More Practical Group Activity Detection: A New Benchmark and Model
• Authors: Dongkeun Kim, Youngkil Song, Minsu Cho, Suha Kwak,
• Abstract summary: Group activity detection (GAD) is the task of identifying members of each group and classifying the activity of the group at the same time in a video. We present a new dataset, dubbed Caf'e, which presents more practical scenarios and metrics. We also propose a new GAD model that deals with an unknown number of groups and latent group members efficiently and effectively.
• Score: 61.39427407758131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Group activity detection (GAD) is the task of identifying members of each group and classifying the activity of the group at the same time in a video. While GAD has been studied recently, there is still much room for improvement in both dataset and methodology due to their limited capability to address practical GAD scenarios. To resolve these issues, we first present a new dataset, dubbed Caf\'e. Unlike existing datasets, Caf\'e is constructed primarily for GAD and presents more practical scenarios and metrics, as well as being large-scale and providing rich annotations. Along with the dataset, we propose a new GAD model that deals with an unknown number of groups and latent group members efficiently and effectively. We evaluated our model on three datasets including Caf\'e, where it outperformed previous work in terms of both accuracy and inference speed.

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