Network PTZ surveillance cameras (Axis)
MSc dissertation proposal 2014/2015
Cooperating
Smart Cameras
Introduction:
The increasing need of surveillance in public spaces and the recent
technological advances on embedded video compression and communications made
camera networks ubiquitous. There are however missing methodologies for
watching so much data captured by so many cameras having few staff. In this
project the main objective is to design methodologies for the automatic
detection and tracking of multiple targets using multiple cooperating cameras.
In addition, current pan-tilt-zoom cameras allow imaging with very high
detail the targets [Starzyk11]. The need to observe multiple targets implies trading-off zoom with keeping the tracking of
targets. A specific objective of the work is to show that more detailed
(zoomed) images can be automatically acquired when there is
lesser people in the area under surveillance.
Objectives:
The main objectives of this project are: (i) calibrating
the cameras composing the camera network, and (ii) detecting novel targets and
maintaining their trajectories.
Detailed description:
This project proposal is focused on detecting and tracking moving
objects, people or vehicles, which cross the fields of view of a network of
cameras. Detection of moving objects is usually performed by background
subtraction or by tracking visual features. Tracking involves motion modelling,
filtering and data association.
Background subtraction usually involves modelling the background. Scene
or illumination changes usually imply background changes that do not correspond
to moving objects. One way to overcome these changes is to model the background
as one, or multiple, gaussians per pixel.
Detecting moving objects by feature tracking is an interesting
alternative when the objects occupy many pixels. In this case the texture of the
objects usually allows detecting and tracking image features (e.g. SIFT or
SURF). The similarities of the trajectories of the tracked features allow
clustering independently moving objects.
Tracking moving objects can be helped by considering motion models. For
instance, when the moving objects are cars or trucks, they cannot move
sideways, and thus one has priors to help filtering. In this case, usually one
uses Extended Kalman Filters (EKF). Considering
multiple objects is handled by using multiple EKFs.
Alternative methodologies include using Particle Filtering or Multiple
Hypothesis Tracking.
In summary, this project proposal involves:
1) Calibrating the cameras forming the network of cameras
2) Detecting moving objects by background subtraction or feature
tracking
3) Assigning a tracking filter to a moving object
4) Tracking multiple objects by using multiple filters
References:
[Hartley00] R. I. Hartley and
[LoweWWW]
David G. Lowe. Demo software: Sift keypoint detector.
http://www.cs.ubc.ca/˜lowe/keypoints/.
[Axis] IP camera manufacturer
actively involved in the definition of the ONVIF standard http://www.axis.com/pt/onvif/index.htm
[ONVIF-www] open industry
forum for the development of a global standard for the interface of IP-based
physical security products http://www.onvif.org/
[Micheloni10] "Video
Analysis in Pan-Tilt-Zoom Camera Networks: From master-slave to cooperative
smart cameras", Christian Micheloni, Bernhard Rinner, and Gian Luca Foresti, IEEE Signal Processing Magazine September 2010
[Starzyk11] "Multi-tasking Smart Cameras for Intelligent Video
Surveillance Systems", Wiktor Starzyk, Faisal Z. Qureshi, 8th
IEEE International Conference on Advanced Video and Signal-Based Surveillance,
2011.
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 surveillance camera setups.
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