Visible to the public Anomaly Detection Algorithm Based on Global Object Map for Video Surveillance System

TitleAnomaly Detection Algorithm Based on Global Object Map for Video Surveillance System
Publication TypeConference Paper
Year of Publication2020
AuthorsShin, H. C., Chang, J., Na, K.
Conference Name2020 20th International Conference on Control, Automation and Systems (ICCAS)
Date PublishedOct. 2020
PublisherIEEE
ISBN Number978-89-93215-20-5
Keywordsabnormal detection, automobiles, Cameras, Deep Learning, deep video, Metrics, pedestrian anomaly, probability, pubcrawl, resilience, Resiliency, Scalability, security, Servers, surveillance, video surveillance
Abstract

Recently, smart video security systems have been active. The existing video security system is mainly a method of detecting a local abnormality of a unit camera. In this case, it is difficult to obtain the characteristics of each local region and the situation for the entire watching area. In this paper, we developed an object map for the entire surveillance area using a combination of surveillance cameras, and developed an algorithm to detect anomalies by learning normal situations. The surveillance camera in each area detects and tracks people and cars, and creates a local object map and transmits it to the server. The surveillance server combines each local maps to generate a global map for entire areas. Probability maps were automatically calculated from the global maps, and normal and abnormal decisions were performed through trained data about normal situations. For three reporting status: normal, caution, and warning, and the caution report performance shows that normal detection 99.99% and abnormal detection 86.6%.

URLhttps://ieeexplore.ieee.org/document/9268258
DOI10.23919/ICCAS50221.2020.9268258
Citation Keyshin_anomaly_2020