Surveillance video summarization based on trajectory rarity measure

Simple item page
dc.contributor.advisorMora Colque, Rensso Victor Hugo
dc.contributor.authorQuispe Torres, Gerar Francis
dc.date.accessioned2019-12-10T16:14:19Z
dc.date.available2019-12-10T16:14:19Z
dc.date.issued2019
dc.description.abstractThe dynamic video summarization of surveillance videos has several critical applications, mainly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, buildings, schools, hospitals, roads, among others. This study presents an approach for the generation of dynamic summary on surveillance video domain based on human trajectories. It has an emphasis on trajectory descriptors in conjunction with the unsupervised clustering method. Our approach contribute to existing literature concerning the combination of methods and objectives. We hypothesize that the clustering of trajectories permits to identify rare trajectories base on their morphology. The clustering as an output provides numerous subsets of trajectories or clusters and the number of elements of a specific cluster is used to determine their rarity. Those subsets with few components are rare while the others that have a high number of elements are considered ordinary; therefore, the implications of our study show that is possible to use unsupervised clustering for automatic detection of rare trajectories based on their morphology and with this information segment videos. We experimented with different sets of trajectories segmenting the rare videos from our ground truth.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.formatapplication/pdfes_PE
dc.identifier.other1072066
dc.identifier.urihttps://hdl.handle.net/20.500.12590/16147
dc.language.isoenges_PE
dc.publisherUniversidad Católica San Pabloes_PE
dc.publisher.countryPEes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectMorphology Trajectory Descriptores_PE
dc.subjectTrajectory Feature Extractiones_PE
dc.subjectDynamic Surveillance Video Summarizationes_PE
dc.subjectTrajectory Clusteringes_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01es_PE
dc.titleSurveillance video summarization based on trajectory rarity measurees_PE
dc.typeinfo:eu-repo/semantics/masterThesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Facultad de Ingeniería y Computaciónes_PE
thesis.degree.levelMaestríaes_PE
thesis.degree.nameMaestro en Ciencia de la Computaciónes_PE
thesis.degree.programEscuela Profesional de Ciencia de la Computaciónes_PE
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
QUISPE_TORRES_GER_SUM.pdf
Size:
13.85 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Tesis - Maestría en Ciencias de la Computación