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- Title
3DMesh-GAR: 3D Human Body Mesh-Based Method for Group Activity Recognition.
- Authors
Saqlain, Muhammad; Kim, Donguk; Cha, Junuk; Lee, Changhwa; Lee, Seongyeong; Baek, Seungryul
- Abstract
Group activity recognition is a prime research topic in video understanding and has many practical applications, such as crowd behavior monitoring, video surveillance, etc. To understand the multi-person/group action, the model should not only identify the individual person's action in the context but also describe their collective activity. A lot of previous works adopt skeleton-based approaches with graph convolutional networks for group activity recognition. However, these approaches are subject to limitation in scalability, robustness, and interoperability. In this paper, we propose 3DMesh-GAR, a novel approach to 3D human body Mesh-based Group Activity Recognition, which relies on a body center heatmap, camera map, and mesh parameter map instead of the complex and noisy 3D skeleton of each person of the input frames. We adopt a 3D mesh creation method, which is conceptually simple, single-stage, and bounding box free, and is able to handle highly occluded and multi-person scenes without any additional computational cost. We implement 3DMesh-GAR on a standard group activity dataset: the Collective Activity Dataset, and achieve state-of-the-art performance for group activity recognition.
- Subjects
HUMAN body; VIDEO surveillance; COLLECTIVE behavior; HUMAN activity recognition
- Publication
Sensors (14248220), 2022, Vol 22, Issue 4, pN.PAG
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22041464