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 an auxiliary moving
device with the capability of estimating of its pose in a global frame. The moving
device 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:
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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.
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Detailed description:
In
order to calibrate each camera of the network we propose using an auxiliary
calibration device localized with respect to a global coordinate system. The device
is equipped with a color-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 I. Zisserman. Multiple view geometry in computer vision.
pages 150–152, 2000.
[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_2018_2019.html