The DOI matches its Crossref record; 153 of the 153 references it cites were found in a registry or an index.
A review of state-of-the-art techniques for abnormal human activity recognition
Abstract
Abstract The concept of intelligent visual identification of abnormal human activity has raised the standards of surveillance systems, situation cognizance, homeland safety and smart environments. However, abnormal human activity is highly diverse in itself due to the aspects such as (a) the fundamental definition of anomaly (b) feature representation of an anomaly, (c) its application, and henceforth (d) the dataset. This paper aims to summarize various existing abnormal human activity recognition (AbHAR) handcrafted and deep approaches with the variation of the type of information available such as two-dimensional or three-dimensional data. Features play a vital role in an excellent performance of an AbHAR system. The proposed literature provides feature designs of abnormal human activity recognition in a video with respect to the context or application such as fall detection, Ambient Assistive Living (AAL), homeland security, surveillance or crowd analysis using RGB, depth and skeletal evidence. The key contributions and limitations of every feature design technique, under each category: 2D and 3D AbHAR, in respective contexts are tabulated that will provide insight of various abnormal action detection approaches. Finally, the paper outlines newly added datasets for AbHAR by the researchers with added complexities for method validations.
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Cites
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Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178
IoT-based smart healthcare video surveillance system using edge computing
Rajavel, R., Ravichandran, S. K., Harimoorthy, K., Nagappan, P., & Gobichettipalayam, K. R. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03157-1 · Cited by 174
Wojtusiak, J., Asadzadehzanjani, N., Levy, C., Alemi, F., & Williams, A. E. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01368-8 · Cited by 26
Metadata
- Authors
- Dhiman, C., & Vishwakarma, D. K.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 77
- Pages
- 21-45
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2893511508
- Cited by
- 265
- References
- 153
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dhiman, C., & Vishwakarma, D. K. (2019). A review of state-of-the-art techniques for abnormal human activity recognition. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2018.08.014
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