||In automated video surveillance applications, presenting the useful information to the human operators is a challenging task. Current systems usually require a prohibitive amount of human resources and lead to a quick decrease of the attention of the human operators through time, thus preventing them to catch the relevant events that may be worth to further investigate. In addition, when monitoring a wide area, it becomes hard to deploy a network of video sensors such that there are enough overlapping FoVs to cover every point of the environment. This leads to the development of video surveillance systems (VSS) that provide partial area coverage. As a result, “blind-gaps” between camera FoVs are introduced. One of the most interesting problems which such “blind-gaps” bring in is to re-identify the persons moving across disjoint FoVs. The contribution of the thesis is two-fold. First, an advanced VSS is designed to display the proper taskdependent information to operators that are monitoring a wide area. In particular, the system helps operators in the task of tracking persons across camera views. This raised the need for a system capable of re-identify the subjects moving through disjoint FoVs. This leads to the second contribution of the thesis, a distributed approach to address the challenges of the person re-identification problem.
||Advisor: Christian Micheloni. PhD thesis defended 9th April 2014, University of Udine
||Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
||other ; abstract ; publishedVersion
Person re-identification ;
Video surveillance ;
||ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 14 Núm. 3 (2015) , p. 39-41 (Special Issue on Recent PhD Thesis Dissemination (2014)) , ISSN 1577-5097