Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702542
Title: Smart video surveillance of pedestrians : fixed, aerial, and multi-camera methods
Author: Climent Perez, Pau
ISNI:       0000 0004 6058 1919
Awarding Body: Kingston University
Current Institution: Kingston University
Date of Award: 2016
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Abstract:
Crowd analysis from video footage is an active research topic in the field of computer vision. Crowds can be analaysed using different approaches, depending on their characteristics. Furthermore, analysis can be performed from footage obtained through different sources. Fixed CCTV cameras can be used, as well as cameras mounted on moving vehicles. To begin, a literature review is provided, where research works in the the fields of crowd analysis, as well as object and people tracking, occlusion handling, multi-view and sensor fusion, and multi-target tracking are analyses and compared, and their advantages and limitations highlighted. Following that, the three contributions of this thesis are presented: in a first study, crowds will be classified based on various cues (i.e. density, entropy), so that the best approaches to further analyse behaviour can be selected; then, some of the challenges of individual target tracking from aerial video footage will be tackled; finally, a study on the analysis of groups of people from multiple cameras is proposed. The analysis entails the movements of people and objects in the scene. The idea is to track as many people as possible within the crowd, and to be able to obtain knowledge from their movements, as a group, and to classify different types of scenes. An additional contribution of this thesis, are two novel datasets: on the one hand, a first set to test the proposed aerial video analysis methods; on the other, a second to validate the third study, that is, with groups of people recorded from multiple overlapping cameras performing different actions.
Supervisor: Not available Sponsor: Kingston University ; European Commission
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.702542  DOI: Not available
Keywords: Computer science and informatics
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