Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/480375
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dc.date.accessioned2023-05-01T08:13:27Z-
dc.date.available2023-05-01T08:13:27Z-
dc.identifier.urihttp://hdl.handle.net/10603/480375-
dc.description.abstractDay-by-day exploring biological diversity caused an exponential increase in population in the last few decades that necessitated the utmost need for safety, security, and management of crowd mobility in urban planning. Manual observing crowd dynamics is a complex enough process, which often produces the wrong results. Understanding crowded sequences greatly interests computer researchers because of its significant concern for research application fields. AI scientists have observed remarkable progress for the past two decades in developing the cognitive comprehension abilities of crowds in video surveillance using computer vision techniques. Posture/light variation, occlusion, shadow effects, and perspective distortion are intrinsic visual challenges leading to crowd analysis of complex phenomena, as these constraints limit the performance of computer vision (CV) mechanisms. newlineThis thesis introduces some self-solution-based deep-learning approaches to simulate crowd behavior in video surveillance. Four research objectives have been examined to investigate crowd dynamics, which focus on density estimation-based crowd counting, the development of automated crowd video surveillance development, and a human facial recognition model with useful motion information. The significant contributions of the thesis are as follows. newlineThe thesis begins with handling the occlusion issue for pedestrian estimation in dense crowd frames and proposed a dynamic kernel convolution neural network-linear regression (DKCNN-LR) model. This model works in two phases; first, a DKCNN model employs convolutional layers in such a way that the kernel weight of each subsequent layer is half the weight of the previous layer. Among all, the first three heavy kernel load layers identify distant camera regions (low-level) features. Later, the lighter kernel weighting layers help identify near-camera region (high-level) features. Second, a linear regression model is employed to perform parametric regression between the actual count (ground truth) and th
dc.format.extent
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleAn Investigation of Videos for Crowd Analysis
dc.title.alternative
dc.creator.researcherTomar, Ankit
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.note
dc.contributor.guideKumar, Santosh and Pant, Bhasker
dc.publisher.placeDehradun
dc.publisher.universityGraphic Era University
dc.publisher.institutionDepartment of Computer Science and Engineering
dc.date.registered2019
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science and Engineering

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01_title.pdfAttached File120.31 kBAdobe PDFView/Open
02_prelim pages.pdf627.71 kBAdobe PDFView/Open
03_contents.pdf81.43 kBAdobe PDFView/Open
04_abstract.pdf69.46 kBAdobe PDFView/Open
05_chapter 1.pdf671.81 kBAdobe PDFView/Open
06_chapter 2.pdf3.03 MBAdobe PDFView/Open
07_chapter 3.pdf8.6 MBAdobe PDFView/Open
08_chapter 4.pdf7.59 MBAdobe PDFView/Open
09_chapter 5.pdf6.47 MBAdobe PDFView/Open
10_chapter 6.pdf7.77 MBAdobe PDFView/Open
11_chapter 7.pdf84.56 kBAdobe PDFView/Open
12_annextures.pdf216.37 kBAdobe PDFView/Open
80_recommendation.pdf206.69 kBAdobe PDFView/Open


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