Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/331495
Full metadata record
DC FieldValueLanguage
dc.coverage.spatialDeveloping new algorithm for anomaly detection in surveillance videos
dc.date.accessioned2021-07-12T10:15:00Z-
dc.date.available2021-07-12T10:15:00Z-
dc.identifier.urihttp://hdl.handle.net/10603/331495-
dc.description.abstractIntelligent video surveillance has received more attention in recent years due to global security concerns. The main objective is to identify or recognize the anomalies present in the videos. In this thesis, an efficient Video Anomaly Detection (VAD) system is presented. It uses Multiple Feature Modalities (MFM) to represent a piece of a rectangular region of predefined size in a video frame called as a patch with Hybrid Classification (HC) using Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers for anomaly detection. The evaluation is done on a set of video sequences from University of California San Diego (UCSD) database which contains anomalies in a pedestrian pathway. As the name MFM implies, three different types of features; raw pixel values, gradient map, and texture energy map are extracted for anomaly detection effectively. At first, the motion estimated frame by background subtraction is partitioned into patches of predefined size. Three different sizes of patches; 15x15, 30x30 and 45x45 are used in this study. From the patches, features are extracted and combined linearly to form the feature vector of the corresponding patch. As SVM and GMM classifiers have their advantages and demerits, an effective VAD system is designed by combing these classifiers using a weighted voting method. The performance of the VAD system is measured in terms of Rate of Detection (RoD), Equal Error rate (EER), and Area Under the Curve (AUC). Initially, the performance of VAD system is analyzed without feature selection by GMM classification newline
dc.format.extentxv,109 p.
dc.languageEnglish
dc.relationp.100-108
dc.rightsuniversity
dc.titleDeveloping new algorithm for anomaly detection in surveillance videos
dc.title.alternative
dc.creator.researcherGnanavel V K
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordanomaly
dc.subject.keywordsurveillance videos
dc.description.note
dc.contributor.guideSrinivasan A
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File28.78 kBAdobe PDFView/Open
02_certificates.pdf240.04 kBAdobe PDFView/Open
03_vivaproceedings.pdf732.21 kBAdobe PDFView/Open
04_bonafidecertificate.pdf479.82 kBAdobe PDFView/Open
05_abstracts.pdf6.22 kBAdobe PDFView/Open
06_acknowledgements.pdf313.13 kBAdobe PDFView/Open
07_contents.pdf6.53 kBAdobe PDFView/Open
08_listoftables.pdf3.54 kBAdobe PDFView/Open
09_listoffigures.pdf6.48 kBAdobe PDFView/Open
10_listofabbreviations.pdf5.16 kBAdobe PDFView/Open
11_chapter1.pdf25.98 kBAdobe PDFView/Open
12_chapter2.pdf102.6 kBAdobe PDFView/Open
13_chapter3.pdf488.05 kBAdobe PDFView/Open
14_chapter4.pdf891.22 kBAdobe PDFView/Open
15_chapter5.pdf110.38 kBAdobe PDFView/Open
16_conclusion.pdf13.96 kBAdobe PDFView/Open
17_references.pdf28.72 kBAdobe PDFView/Open
18_listofpublications.pdf89.13 kBAdobe PDFView/Open
80_recommendation.pdf49.21 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: