The project aims at building a flexible active video surveillance platform able to automatically collect data and detect events or the potential threats, designed to assist security operators in their decisions. The platform has to handle all tasks related to the automatic management of a video surveillance system, thus relieving the operator from routine checks and permitting her/him to concentrate solely on abnormal events reported by the system. The goal is to implement a modular, flexible, and scalable architecture able to adapt to different operational scenarios.
TeV role is the development and implementation of various computer vision modules to be integrated in the video surveillance platform: background updating, people detection and tracking, people re-identification in different camera streams.
Duration: from July 2011 to September 2013
Person re-identification in camera networks consists in matching observations of individuals across disjoint views in a network of surveillance cameras.
In this project TeV developed a single-shot re-identification module and integrated it in the video surveillance system, in order to propose hypothesis of re-identifications. It is a supervised method to compute a scoring function that, when applied to a pair of images, provides a score expressing the likelihood that they depict the same individual.
Using the implemented person detection and tracking modules we obtain multiple descriptors of the same person, therefore producing a more robust output in the re-identification task.
S. Messelodi, C.M. Modena, Boosting Fisher Vector based Scoring Functions for Person Re-Identification, Image and Vision Computing, Vol. 44, pp. 44-58, 2015
N. Conci, F.G.B. De Natale, S. Messelodi, C.M. Modena, M. Verza, and R. Fioravanti. An integrated framework for video surveillance in complex environments. IEEE International Smart Cities Conference - ISC2, 2016
Contact: Stefano Messelodi
Team: Stefano Messelodi, Carla Maria Modena