State-of-the-art violence detection techniques in video surveillance security systems: a systematic review.
| Title: | State-of-the-art violence detection techniques in video surveillance security systems: a systematic review. |
|---|---|
| Authors: | Omarov B; Alem Research, Almaty, Kazakhstan.; International University of Tourism and Hospitality, Turkistan, Kazakhstan.; Suleiman Demirel University, Almaty, Kazakhstan.; Al-Farabi Kazakh National University, Almaty, Kazakhstan.; Narynov S; Alem Research, Almaty, Kazakhstan.; Zhumanov Z; Alem Research, Almaty, Kazakhstan.; Al-Farabi Kazakh National University, Almaty, Kazakhstan.; Gumar A; Alem Research, Almaty, Kazakhstan.; Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan.; Khassanova M; Alem Research, Almaty, Kazakhstan.; Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan. |
| Source: | PeerJ. Computer science [PeerJ Comput Sci] 2022 Apr 06; Vol. 8, pp. e920. Date of Electronic Publication: 2022 Apr 06 (Print Publication: 2022). |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: PeerJ Inc Country of Publication: United States NLM ID: 101660598 Publication Model: eCollection Cited Medium: Internet ISSN: 2376-5992 (Electronic) Linking ISSN: 23765992 NLM ISO Abbreviation: PeerJ Comput Sci Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: San Diego, CA : PeerJ Inc., [2015]- |
| Abstract: | We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods' performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.; (©2022 Omarov et al.) |
| Competing Interests: | Batyrkhan Omarov, Sergazi Narynov, and Zhandos Zhumanov are employees of Alem Research. |
| References: | IEEE Trans Pattern Anal Mach Intell. 2018 Jun;40(6):1437-1451. (PMID: 28622667); IEEE Trans Pattern Anal Mach Intell. 2007 Dec;29(12):2247-53. (PMID: 17934233); PLoS One. 2015 Apr 10;10(4):e0120448. (PMID: 25860667); BMC Med Res Methodol. 2018 Nov 19;18(1):143. (PMID: 30453902); PLoS One. 2018 Oct 3;13(10):e0203668. (PMID: 30281588); IEEE Trans Pattern Anal Mach Intell. 2019 Nov;41(11):2740-2755. (PMID: 30183621); IEEE Trans Pattern Anal Mach Intell. 2015 Nov;37(11):2346-59. (PMID: 26440272); Sensors (Basel). 2019 May 30;19(11):. (PMID: 31151184); IEEE Trans Image Process. 2018 Jun 08;27(10):4787-4797. (PMID: 29994215); Comput Intell Neurosci. 2021 Jul 26;2021:7367870. (PMID: 34354745); Neural Comput Appl. 2017;28(Suppl 1):565-572. (PMID: 29213188); Sensors (Basel). 2020 Apr 03;20(7):. (PMID: 32260274) |
| Contributed Indexing: | Keywords: Artificial intelligence; Computer vision; Datasets; Deep learning; Machine learning; Video features; Violence detection |
| Entry Date(s): | Date Created: 20220502 Latest Revision: 20240827 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC9044356 |
| DOI: | 10.7717/peerj-cs.920 |
| PMID: | 35494848 |
| Database: | MEDLINE |
Journal Article