Title: Comprehensive survey on video anomaly detection using deep learning techniques

Authors: Sreedevi R. Krishnan; P. Amudha; S. Sivakumari

Addresses: Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India ' Department of Computer Science and Engineering, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India

Abstract: The rapid increase in violence and crime leads to the use of video surveillance systems. Handling such huge videos and classifying them as abnormal or not are tedious. Therefore, an automatic anomaly detection method is vital for the real-time detection of anomalous events. Advancements in machine intelligence lead to an automatic anomaly detection system for the timely identification of anomalous events and reducing the after-effects. Recent research uses deep learning techniques for faster and automatic detection of abnormal events from an enormous volume of surveillance videos. Reviewing the video anomaly detection system is very relevant and helps to promote future research in this area. The paper performs a comprehensive study of several video anomaly detection methods using deep learning techniques to detect and predict anomalous events. The paper also surveys various methods used for women's safety. Various methodologies, datasets, and evaluation metrics for detecting video anomalies and comparisons are included.

Keywords: deep learning; CNN; LSTM; GAN; autoencoder; women safety.

DOI: 10.1504/IJCVR.2024.139544

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.4, pp.445 - 466

Received: 03 Jan 2022
Accepted: 05 Oct 2022
Published online: 04 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article