The DOI matches its Crossref record; 498 of the 498 references it cites were found in a registry or an index.
Human Action Recognition From Various Data Modalities: A Review
Open accessAbstract
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this article, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.
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Cited by
Spatio-temporal segments attention for skeleton-based action recognition
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Metadata
- Authors
- Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022
- Volume
- 45
- Issue
- 3
- Pages
- 1-20
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W4282981352
- Cited by
- 601
- References
- 498
- FWCI
- 43.87
- Type
- journal article
- Language
- en
- Open access
- green
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