detect anomalies in surveillance videos :survey

Paper ID : 1055-ISCBAS

Authors

Esraa A Mahareek * teaching assistance

Abstract

This survey article provides a comprehensive overview of various techniques for detecting anomalies in surveillance videos. These techniques include both traditional methods, such as statistical modeling and motion analysis, as well as more recent approaches that incorporate deep learning and artificial intelligence. The survey also highlights the strengths and limitations of each technique, as well as their potential applications in real-world scenarios. Additionally, the survey discusses the challenges in designing effective anomaly detection systems for surveillance videos and identifies future research directions. Overall, this survey serves as a valuable resource for researchers and practitioners working in the field of video surveillance and anomaly detection. It offers insights into the current state of research on anomaly detection in surveillance videos and presents a roadmap for future studies. It is important to note that the success of anomaly detection in surveillance videos relies heavily on the availability and quality of training data, as well as careful feature selection and parameter tuning to ensure optimal performance of the anomaly detection system. Therefore, future research should focus on developing robust feature extraction techniques and improving the interpretability of anomaly detection models. Moreover, the survey suggests that future studies should also explore novel techniques for improving the scalability and efficiency of anomaly detection systems in surveillance videos.

Keywords

artificial intelligence, computer vision, detecting anomalies, deep learning

Status: Abstract Accepted (Poster Presentation)