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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Frontier video anomaly detection methods based on deep learning: comprehensive review

Li Nanjun1,2
Nie Xiushan3
Li Tuo1,2
Zou Xiaofeng1,2
Wang Changhong1,2
1. Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. , Ltd. , Jinan 250013, China
2. Shandong Inspur Artificial Intelligence Research Institute Co. , Ltd. , Jinan 250013, China
3. School of Computer Science & Technology, Shandong Jianzhu University, Jinan Shandong 250101, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0241

Abstract

Video anomaly detection (VAD) is one of the hottest research topics in the field of computer vision, which is significant for research and valuable for application. In recent years, inspired by the outstanding performances of deep learning technologies represented by the convolution neural networks on various tasks of machine vision, a large number of deep learning-based VAD researches have rapidly emerged. To this end, this study comprehensively sorts out and systematically summarizes the deep learning-based VAD researches. Firstly, it proposes a multi-level classification scheme based on the three core elements of anomaly detection process including detection strategy, sample setting and learning/inferring mechanism, which is utilized to summarize the frontier deep learning-based VAD methods by class, refines the mathematical models of representative algorithms and elaborates the limitations of existing works simultaneously; Secondly, it introduces the benchmark datasets of video anomaly detection and compares the performances of advanced methods on diverse datasets; Finally, it discusses the future research directions in four aspects as follows: complex lighting/weather conditions, fusion of multi-modal images, semantic interpretability and adaptive scene perception, which is expected to provide references for future research works in this field.

Foundation Support

山东省自然科学基金青年基金项目(ZR2023QF050,ZR2023QF056)
国家自然科学基金项目(62176141)
山东省自然科学基金杰出青年基金项目(ZR2021JQ26)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0241
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-11-05] Accepted Paper

Cite This Article

李南君, 聂秀山, 李拓, 等. 基于深度学习的前沿视频异常检测方法综述 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.06.0241. (Li Nanjun, Nie Xiushan, Li Tuo, et al. Frontier video anomaly detection methods based on deep learning: comprehensive review [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.06.0241. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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