On a Distributed Video Surveillance System to Track Persons in Camera Networks

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.