Smart video surveillance of pedestrians : fixed, aerial, and multi-camera methods

Climent Perez, Pau (2016) Smart video surveillance of pedestrians : fixed, aerial, and multi-camera methods. (PhD thesis), Kingston University, .

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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.

Item Type: Thesis (PhD)
Additional Information: This work has been supported by the European Commission's Seventh Framework Programme (FP7-SEC-2011-1) under grant agreement no. 285320 (PROACTIVE project).
Physical Location: This item is held in stock at Kingston University library.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017) > Digital Imaging Research Centre (DIRC)
Depositing User: Jennifer May
Date Deposited: 03 Feb 2017 15:36
Last Modified: 06 Nov 2018 10:16
URI: http://eprints.kingston.ac.uk/id/eprint/37298

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