Network surveillance cameras (Axis), People Tracking (Caviar dataset)
MSc dissertation proposal 2013/2014
Vision Based
Multi-Target Detection and Tracking
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 cameras.
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