CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks

• Published in: Multimedia tools and applications Vol. 80; no. 11; pp. 16979 - 16995
• Main Authors: Ullah, Waseem, Ullah, Amin, Haq, Ijaz Ul, Muhammad, Khan, Sajjad, Muhammad, Baik, Sung Wook
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Anomalies; Artificial neural networks; Complexity; Computer Communication Networks; Computer Science; Computer vision; Crime; Data Structures and Information Theory; Datasets; Decision making; Feature extraction; Frames (data processing); Multilayers; Multimedia Information Systems; Public safety; Special Purpose and Application-Based Systems; Surveillance; Surveillance systems; Video data; Deep learning; Intelligent surveillance networks; LSTM; Smart surveillance; Crime detection; Anomaly detection

In current technological era, surveillance systems generate an enormous volume of video data on a daily basis, making its analysis a difficult task for computer vision experts. Manually searching for unusual events in these massive video streams is a challenging task, since they occur inconsistently...