Work

An explainable and efficient deep learning framework for video anomaly detection

Year: 2021

Type: article

Source: Cluster Computing

DOI: 10.1007/s10586-021-03439-5

Authors: Chongke Wu, Sicong Shao, Cihan Tunc, Pratik Satam, Salim Hariri

Institutions: University of Arizona, University of North Texas

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Energy, National Institute of Standards and Technology, National Nuclear Security Administration, National Science Foundation of Sri Lanka, Air Force Office of Scientific Research

Awards: G1523888516, RESILIENT APPLICATION AS A SERVICE (RAAAS), G2439292468, Cybersecurity Scholarship-for-Service at The Unive, G3873129418, G4050371125, G4783735114, G6455465449, I/UCRC: Phase II: Industry/University Cooperative Research Center for Cloud and Autonomic Computing (CAC), UNIVERSITY OF ARIZONA PARTNERSHIP FOR PROACTIVE CYBERSECURITY TRAINING (PACT) THIS NEW AWARD PROVIDES FULL FUNDING TO THE RECIPIENT IN THE AMOUNT OF $2,929,230.00 OF WHICH $997,792 FULLY FUNDS BUDGET PERIOD 1., FCTaaS: Federated Cybersecurity Testbed as a Service

OpenAlex ID: W3216015240

FWCI: 4.252

Cites: 57

Cited by: 88

Related to: 10