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Mora Colque, Rensso Victor Hugo
Quispe Torres, Gerar Francis
2019-12-10T16:14:19Z
2019-12-10T16:14:19Z
2019-12-10T16:14:19Z
2019-12-10T16:14:19Z
2019
1072066
https://hdl.handle.net/20.500.12590/16147
The 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)
Trabajo de investigación (es_PE)
application/pdf (es_PE)
eng (es_PE)
Universidad Católica San Pablo (es_PE)
SUNEDU
info:eu-repo/semantics/openAccess (es_PE)
https://creativecommons.org/licenses/by/4.0/ (es_PE)
Universidad Católica San Pablo (es_PE)
instacron:UCSP
instname:Universidad Católica San Pablo
reponame:UCSP-Institucional
Repositorio Institucional - UCSP (es_PE)
Dynamic Surveillance Video Summarization (es_PE)
Trajectory Feature Extraction (es_PE)
Morphology Trajectory Descriptor (es_PE)
Trajectory Clustering (es_PE)
Surveillance video summarization based on trajectory rarity measure (es_PE)
info:eu-repo/semantics/masterThesis (es_PE)
Universidad Católica San Pablo. Facultad de Ingeniería y Computación (es_PE)
Ciencia de la Computación (es_PE)
Maestría (es_PE)
Maestro en Ciencia de la Computación (es_PE)
PE (es_PE)
http://purl.org/pe-repo/ocde/ford#1.02.01 (es_PE)
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