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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/16147
Title: Surveillance video summarization based on trajectory rarity measure
Authors: Quispe Torres, Gerar Francis
metadata.dc.contributor.advisor: Mora Colque, Rensso Victor Hugo
Keywords: Morphology Trajectory Descriptor;Trajectory Feature Extraction;Dynamic Surveillance Video Summarization;Trajectory Clustering
Issue Date: 2019
Publisher: Universidad Católica San Pablo
Abstract: 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.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/16147
Appears in Collections:Tesis Postgrado - Maestría en Ciencia de la Computación

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