Objectives:
The main objective
of the work consists in estimating the position, the orientation and the
intrinsic parameters (calibration) of each camera within a network of cameras,
possibly with non-overlapping fields of view. Calibration comprises both the
intrinsic and extrinsic parameters. The non-overlapping problem is considered
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, and therefore transfer the calibration to the networked
camera.
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
Requirements (grades, required courses, etc):
Localization:
ISR/IST
Observations:
-- 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
difficult without the aid of special equipment (moving calibration patterns or
GPS) or a priori reference images such as a panorama of the given environment.
-- Detailed
description:
In order to calibrate
each camera of the network we propose using a robot localized with respect to a
global coordinate system. The robot is equipped with a colour-depth
camera which can be pointed to a field of view similar to the one observed by
the network (fixed) camera.
Given a number of
3D-points of the environment and their images we can calibrate one camera using
standard computer vision methodologies [Hartley00, Leite08]. Using 3D lines in
the scene and imaged by the camera, one can obtain more accurate calibrations
[Silva12]. Developing automatic correspondences of 3D lines with 2D imaged
lines, possibly using automatic SIFT based matching of 3D points and 2D points
[Lowe04], is an interesting and practical contribution for the calibration of
network cameras.
Work-proposal
detailed steps:
- Testing the
detection and correspondence of SIFT features in simulated and real scenarios.
- Testing the
detection and correspondence of 3D lines of the scene with imaged 2D lines.
- Testing the
camera calibration methodologies based on the information obtained in the
previous steps.
References:
[Silva12] M.
Silva, R. Ferreira and J. Gaspar. Camera Calibration using a Color-Depth
Camera: Points and Lines Based DLT including Radial Distortion. In WS in Color-Depth Camera Fusion in Robotics, held with IROS
2012.
[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.
[Hartley00] R. I. Hartley and
[Lowe04] David G.
Lowe. Distinctive image features from scale-invariant keypoints.
In International Journal of Computer Vision, pages 91–110, 2004.
-- More information in:
http://users.isr.ist.utl.pt/~jag/msc/msc_2017_2018.html