Look-up in Google Scholar
Title: Surveillance video summarization based on trajectory rarity measure
Advisor(s): Mora Colque, Rensso Victor Hugo
OCDE field: http://purl.org/pe-repo/ocde/ford#1.02.01
Issue Date: 2019
Institution: 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.
Discipline: Ciencia de la Computación
Grade or title grantor: Universidad Católica San Pablo. Facultad de Ingeniería y Computación
Grade or title: Maestro en Ciencia de la Computación
Register date: 10-Dec-2019; 10-Dec-2019



This item is licensed under a Creative Commons License Creative Commons