Network cameras
MSc dissertation proposal 2011/2012
Network
Cameras Calibration
Introduction:
The increasing need of surveillance in public spaces and the recent
technological advances on embedded video compression and communications made camera
networks ubiquitous. Typical environments include single rooms, complete
buildings, streets, highways, tunnels, etc. While the technological advances
already allowed such a wide installation of camera networks, the automatically
extracting information from the video streams is still an active research area.
One of the crucial problems in camera networks is to obtain a correct
calibration of each camera in terms of a unique, global reference frame. This condition is a fundamental feature
required for further higher level processing (i.e. people/car tracking, event
detection, metrology) and nowadays one of the most
active research subjects in Computer Vision. The problematics
generally arise from the lack of
overlapping field of views (FOV) of the camera which does not allow the
estimation of a common reference frame for each camera. In such scenario,
exactly geolocating each sensor is extremely complex
without the aid of special equipment (moving calibration patterns or GPS) or a
priori reference images such as a panorama of the given environment.
Objectives:
The main objective of the work consists in estimating the calibration of
a network of cameras, possibly with non-overlapping fields of view. Calibration
comprises both the intrinsic and
extrinsic parameters of the cameras. The non-overlapping problem is
expected to be overcome using a mobile robot with the capability of estimating
of its pose in a global frame. The robot is equipped with one calibrated camera
which we assume that can be oriented in a manner to observe world points also
seen by the network of cameras.
Detailed description:
In order to calibrate the camera network we propose an approach based on
the visual reconstruction of some points of the scenario. These points are
expressed in a unique, global (also termed world) coordinate frame provided by
self-localization information defined by the starting location of a mobile
robot. Given a number of reconstructed 3D-points of the environment and their
images we can calibrate the network of cameras using standard computer vision
methodologies [Hartley00]. The reconstruction of 3D points comprises two main
steps, namely matching image points and computing their locations. We do the
matching based on Scale Invariant Feature Transform (SIFT) features, state of
the art features well known to provide a very robust matching procedure [LoweWWW, Lowe04], and the computation of the 3D locations
is based on Visual Simultaneous Localization and Map Building (vSLAM) [Goncalves05, Karlsson05].
Work-proposal detailed steps:
(i) Studying and testing vSLAM,
by doing the reconstruction of points and trajectories in some simulated or
real scenarios, based on various software packages freely available in the
Internet.
(ii) Testing the detection and correspondence of SIFT in simulated or
real scenarios. Studying some methodologies to improve the precision of the
registration of the SIFT features.
(iii) Testing the camera calibration methodologies based on the
information obtained in the previous steps.
References:
[Leite08] Calibrating a Network of Cameras Based
on Visual Odometry, Nuno
Leite, Alessio Del Bue, José Gaspar, in Proc. of
IV Jornadas de Engenharia Electrónica e Telecomunicações e de Computadores,
pp174-179, November 2008, Lisbon,
Portugal.
[Goncalves05] L. Goncalves, E. Di Bernardo, D.
Benson, M. Svedman, J. Ostrowski,
N. Karlsson, and P. Pirjanian.
A visual front-end for simultaneous localization and mapping.
In International Conference on Robotics and Automation, 2005.
[Karlsson05] N. Karlsson, E. Di Bernardo, J. Ostrowski, L. Goncalves, P. Pirjanian, and M. Munich. The vslam algorithm for robust localization and mapping.
In International Conference on Robotics and Automation, 2005.
[Hartley00] R. I. Hartley and
[LoweWWW] David G. Lowe. Demo software: Sift keypoint detector. http://www.cs.ubc.ca/˜lowe/keypoints/.
[Lowe04] David G. Lowe. Distinctive image features from scale-invariant keypoints. In International Journal of Computer Vision,
pages 91–110, 2004.
Requirements (grades, required courses, etc):
-
Expected results:
At the end of the work, the students will have enriched their experience
in computer vision applied to camera network setups. In particular are expected
to develop and assess:
- precise image (feature) registration
methodologies
- precise calibration methodologies
Place for conducting the work-proposal:
ISR / IST
More MSc dissertation
proposals on Computer and Robot Vision in:
http://omni.isr.ist.utl.pt/~jag