Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/345433
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dc.coverage.spatialCertain investigations on pedestrian detection using multiresolution analysis and deep learning
dc.date.accessioned2021-10-25T05:01:26Z-
dc.date.available2021-10-25T05:01:26Z-
dc.identifier.urihttp://hdl.handle.net/10603/345433-
dc.description.abstractIn computer vision, pedestrian detection is a rapidly growing researcharea due to the importance of real time applications such as video surveillancesystem, person identification, automated driver assistance and automotivesafety. Due to the vulnerability of pedestrians in heavy traffic, especially inurban areas, the detection of pedestrians becomes essential for road safety.The existing techniques of Deformable Part Model, extended deep model,RealBoost method and Deep Neural Networks have been employed indifferent scenarios for pedestrian detection. To improve the accuracy, reducethe false positive rate, memory requirement and time consumption (trainingand response time) over the existing methods, three novel techniques namely Multiresolution Morlet Decomposition based Iterative Learning DeformablePart (MMD-ILDP) model, Soft sign Gaussian Recurrent Deep Neural Network(SGRDNN) technique and Multi-Resolution Deformable Part Based Fully Recurrent Deep Neural Learning (MDP-FRDNL) have been proposed in this research work. newline
dc.format.extentxxvi,167p
dc.languageEnglish
dc.relationp.157-166
dc.rightsuniversity
dc.titleCertain investigations on pedestrian detection using multiresolution analysis and deep learning
dc.title.alternative
dc.creator.researcherGovardhan, S D
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Interdisciplinary Applications
dc.subject.keywordReal time applications
dc.subject.keywordPedestrian detection
dc.subject.keywordDeep learning
dc.description.note
dc.contributor.guideVasuki, A
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registeredn.d.
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

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01_title.pdfAttached File198.2 kBAdobe PDFView/Open
02_certificates.pdf168.74 kBAdobe PDFView/Open
03_vivaproceedings.pdf429.4 kBAdobe PDFView/Open
04_bonafidecertificate.pdf247 kBAdobe PDFView/Open
05_abstracts.pdf16.83 kBAdobe PDFView/Open
06_acknowledgements.pdf284.25 kBAdobe PDFView/Open
07_contents.pdf307.07 kBAdobe PDFView/Open
08_listoftables.pdf124.54 kBAdobe PDFView/Open
09_listoffigures.pdf300.47 kBAdobe PDFView/Open
10_listofabbreviations.pdf187.54 kBAdobe PDFView/Open
11_chapter1.pdf248.78 kBAdobe PDFView/Open
12_chapter2.pdf196.87 kBAdobe PDFView/Open
13_chapter3.pdf410.27 kBAdobe PDFView/Open
14_chapter4.pdf427.19 kBAdobe PDFView/Open
15_chapter5.pdf520.42 kBAdobe PDFView/Open
16_chapter6.pdf580.03 kBAdobe PDFView/Open
17_conclusion.pdf21.04 kBAdobe PDFView/Open
18_references.pdf181.05 kBAdobe PDFView/Open
19_listofpublications.pdf125.1 kBAdobe PDFView/Open
80_recommendation.pdf64.77 kBAdobe PDFView/Open


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