Title: Generalised video anomaly detection: a systematic review

Authors: S. Anjali; S. Don

Addresses: Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, India ' Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, India

Abstract: The practice of identifying irregularities and outliers in data is known as anomaly detection. Due to the demand for prompt and precise anomaly detection, this is a growing research area in computer vision. The purpose of this paper is to provide a systematic literature review (SLR) on video anomaly detection by creating the pertinent research questions (RQs). We have considered 83 research articles from reputable databases published between 2012 and 2023. After reviewing these publications, we developed a taxonomy of different video anomaly detection strategies and found that deep learning-based algorithms performed better than traditional ones. The two most common applications of video anomaly detection are seen in the surveillance and healthcare domains. We have identified 16 benchmark datasets, including surveillance and medical datasets. Researchers can use this SLR to look into the most recent studies, applications, datasets, methodologies, challenges and future scope of video anomaly detection.

Keywords: video anomaly detection; visual anomaly detection; computer vision; deep learning; systematic literature review; SLR.

DOI: 10.1504/IJCVR.2025.147490

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.4, pp.431 - 458

Received: 16 Jul 2023
Accepted: 15 Nov 2023
Published online: 18 Jul 2025 *

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