The DOI matches its Crossref record; 256 of the 256 references it cites were found in a registry or an index.
A Review of Human Activity Recognition Methods
Open accessAbstract
Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.
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Cites
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Cited by
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Multi-user activity recognition: Challenges and opportunities
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Automating medical simulations
Gershov, S., Braunold, D., Spektor, R., Ioscovich, A., Raz, A., & Laufer, S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104446 · Cited by 8
Metadata
- Authors
- Vrigkas, M., Nikou, C., & Kakadiaris, I. A.
- Venue
- Frontiers in Robotics and AI
- Publisher
- Frontiers Media SA
- Published
- 2015-11-16
- Volume
- 2
- ISSN
- 2296-9144 (ISSN-L)
- OpenAlex
- W2180635266
- Cited by
- 566
- References
- 256
- FWCI
- 23.40
- Type
- journal article
- Language
- en
- Open access
- gold
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- Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015). A Review of Human Activity Recognition Methods. Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2015.00028
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