Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model

dc.creatorMansour, Romany F.
dc.creatorEscorcia-García, José
dc.creatorGamarra, Margarita
dc.creatorVILLANUEVA, JAIR ASIR
dc.creatorLeal, Nallig
dc.date2021-06-22T19:45:58Z
dc.date2021-06-22T19:45:58Z
dc.date2021
dc.date2023
dc.date.accessioned2023-10-03T19:32:57Z
dc.date.available2023-10-03T19:32:57Z
dc.identifier0262-8856
dc.identifierhttps://hdl.handle.net/11323/8394
dc.identifierhttps://doi.org/10.1016/j.imavis.2021.104229
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170609
dc.descriptionRecently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim of anomaly detection is to automatically determine the existence of abnormalities in a short time period. Deep reinforcement learning (DRL) techniques can be employed for anomaly detection, which integrates the concepts of reinforcement learning and deep learning enabling the artificial agents in learning the knowledge and experience from actual data directly. With this motivation, this paper presents an Intelligent Video Anomaly Detection and Classification using Faster RCNN with Deep Reinforcement Learning Model, called IVADC-FDRL model. The presented IVADC-FDRL model operates on two major stages namely anomaly detection and classification. Firstly, Faster RCNN model is applied as an object detector with Residual Network as a baseline model, which detects the anomalies as objects. Besides, deep Q-learning (DQL) based DRL model is employed for the classification of detected anomalies. In order to validate the effective anomaly detection and classification performance of the IVADC-FDRL model, an extensive set of experimentations were carried out on the benchmark UCSD anomaly dataset. The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
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dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceImage and Vision Computing
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S0262885621001347
dc.subjectVideo surveillance
dc.subjectIntelligent systems
dc.subjectAnomaly detection
dc.subjectDeep reinforcement learning
dc.subjectUCSD dataset
dc.titleIntelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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