D. Bloisi, L. Iocchi, G.R. Leone, R.
(Automatic Remote Grand Canal Observation System) is a
video-surveillance system for boat traffic monitoring, measurement and
management along the Grand Canal of Venice. This new system will answer
to the specific requirements for the boat navigation rules in Venice
while providing a combined unified view of the whole Grand Canal
waterway. Such features far exceed the performance of any
product. Therefore, a specific software has been developed, based on the
integration of advanced automated image analysis
The ARGOS system is going to control a waterway of about 4 km length,
80 to 150 meters width, through 14 observation points (Survey Cells).
The system is based on the use of groups of IR/VIS cameras, installed
just below the roof of several buildings leaning over the Grand Canal. Each
survey cell is composed of 4 optical sensors: one center wide-angle (90
degree), orthogonal to the navigation axis, two side
deep-field cameras (50-60 degree), and a pan-tilt-zoom camera for high
resolution acquisition of boat details (e.g., license plates).
The main ARGOS functions are: 1) Optical detection and tracking of
moving targets present in the FOV; 2) Computing position, speed and
heading of any moving target within the FOV of each camera; 3)
survey cell level of any event (target appears, exits, stops, starts
within the cells FOV) and transmission of any event to the Control
Center; 4) Connecting all
the track segments related to the same target in the different cameras
FOV into a unique trajectory and track ID; 5) Recording all the video
together with the graphical information related to track IDs and
trajectories; 6) Rectifying all the camera frames and stitching them
into a composite plain image so as to show a plan view of the whole
Grand Canal; 7) Allowing the operator to graphically select any target
detected by the system and automatically activating the nearest PTZ
camera to track the selected target.
Main techniques used for image
analysis and tracking
SEGMENTATION BASED ON
tracking problem considered in this project has been solved by using a set of Kalman
filters and a Nearest Neighbors approach to data association. Moreover, in
order to consider uncertainty in data association and filtering, a
multi-hypothesis tracking has been implemented. The steps of the filter
are: track formation, track update, track split, track merge, track
deletion. These are determined by evaluating the parameters of the
Image rectification is
used to produce a panoramic view for each cell (FOV larger than 180
degrees) and for a top-view image that is stitched on top of
GIS and orthophoto images. Rectification also allows
for converting image coordinates into metric coordinates (in
particular, we use Gauss-Boaga coordinates) and thus to
geo-referentiate the boats in the Grand Canal and estimate their
Tracking demonstration (speed
up) 9 MB
RAI-TG1 November 2007 (In Italian)
Independent Multimodal Background Subtraction.
D. Bloisi, L. Iocchi.
In Proc. of the Third Int. Conf. on Computational Modeling of Objects
Presented in Images: Fundamentals, Methods and Applications,, pp. 39-44, 2012.
Automatic Real-Time River Traffic Monitoring Based on Artificial Vision Techniques.
L. Iocchi, L. Novelli, L. Tombolini, M. Vianello.
In International Journal of Social Ecology and Sustainable Development (IJSESD),
volume 1(2), pp. 40-51, 2010.
ARGOS - A Video Surveillance System for Boat Trafic Monitoring in Venice.
Bloisi, L. Iocchi.
In International Journal of Pattern Recognition and Artificial Intelligence. Vol. 3(7), pp. 1477-1502, 2009.
k-means based clustering algorithm.
Bloisi, L. Iocchi.
In Computer Vision Systems,
volume 5008 of LNCS, pages 109--118. Springer, 2008.
A Distributed Vision System for Boat Traffic
Monitoring in the Venice Grand Canal.
Bloisi, D., Iocchi, L.,
Leone, G. R., Pigliacampo, R., Tombolini, L., and Novelli, L..
In Proc. of 2nd Int.
Conf. on Computer Vision Theory and Applications (VISAPP). 2007.
pp. 549--556. Note:
Moving Objects with Unreliable Sensors for Mobile Robotic Platforms.
Extended abstract from Master Thesis. University of Rome “La
The project has been
realized thanks to the view of the future and to the active
participation of the City Council of Venice. In particular, special
thanks to Lord Vice-Major of Venice, On. Michele Vianello, for his
foresight in applying innovative technologies in the delicate and
complex historical city as Venice. We are also grateful to the
Responsible Manager Arch. Manuele Medoro and his staff for their
constant support and commitment.