A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets
Open accessAbstract
Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.
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Some survey papers highlight the benefits of using specific datasets in VSSs.
“Some survey papers highlight the benefits of using specific datasets in VSSs [8,9].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [9]
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References 223 · Cited by 114
Research works printed but not linked: 1
- B. D. Rouhani, A. Mirhoseini, and F. Koushanfar, ''RISE: An automated framework for real-time intelligent video surveillance on FPGA,'' ACM Trans. Embedded Comput. Syst., vol. 16, no. 5s, 2017, Art. no. 158. GURUH FAJAR SHIDIK was born in West Borneo, Indonesia, in February 1987. He has received the Bachelor of Computer Science degree from Universitas Dian Nuswantoro (UDINUS), Semarang, Indonesia, in 2009, the Master of Computer Science degree from Technical Malaysia Melaka University (UTeM), in 2011, the Ph.D. degree from Universitas Gadjah Mada (UGM), Indonesia, in 2016. He is currently a Lecturer with UDINUS. His areas of interests include cloud computing, wireless communication, machine learning, and artificial intelligence.
- Authors
- Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J.
- Affiliations
- Universitas Dian Nuswantoro (ID)
- ORCID
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 170457-170473
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2992009433
- Cited by
- 114
- References
- 223
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 23 · 2023: 28 · 2022: 24 · 2021: 16 · 2020: 6
- FWCI
- 1.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Direktorat Riset dan Pengabdian Masyarakat, Direktorat Jendral Penguatan Riset, Teknologi, dan Pendidikan Tinggi, Indonesia (026/L6/AK/SP2H.1/PENELITIAN/2019, 060/A38.04/UDN-09/V/2019)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- video surveillance, research trends, network infrastructure design, multimedia data
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