Artificial Vision
LOGICAL ARCHITECTURE
Dr. Christian Micheloni
Department of Computer Science
University of Udine, ITALY
Artificial Vision
State of the art
• First Generation of Surveillance Systems with analog data
Transmisison (1960-1980).
Control center
Multiplexer
Analog
transceiver
Sensor
level
Local processing
level
Klagenfurt 6-11 April
Network level
(coax 75 ohm
cable network)
2011
PAGE 2
Analog
storage devices
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Second Systems
Generation
(1990-2000)
State of the art (2)
IEEE
802.11
Cable modem
HDSL
modem
Sensor
level
Signal Processing
Network
Local signal
processing
(channel
HUB
WAN o LAN
coding)
Network level
Klagenfurt 6-11 April
PC’s Cluster
(attention driving)
2011
PAGE 3
Operator level
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Third Systems
Generation
(2000- ???)
State of the art (3)
UMTS
Cable modem
ADSL
modem
Sensor
Network
HUB
level
Video processing
and recognition at
the sensor level
Klagenfurt 6-11 April
Local signal
processing
(channel
WAN o LAN
Network level
coding)
2011
PAGE 4
Decision planning
Operator level
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Logical architecture
Image Acquisition
Frame Grabber HW
Frame Grabber SW
Frame Corrente
Creation and
Updating of
Background
Change Detectioni
Motion Analysis
Focus of Attention
Tracking
Feature
extraction
Localization
GRAPHIC INTERFACE
Motion Detection
Event Analysis
Identification
Database
Classification
ALARM
Klagenfurt 6-11 April
2011
PAGE 5
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Background Updating
Change Detection
Bck (t )
Current
Frame
I (t )
Change Detection
Delay
Delay
I (t  1)
Klagenfurt 6-11 April
I t  1   Bck t  1  I t  1
Bck (t  1)
Background Updating
2011
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Mobile object detection
- Detection
- Localization
- Tracking
Klagenfurt 6-11 April
2011
PAGE 7
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object detection
Change
Detection
Klagenfurt 6-11 April
2011
PAGE 8
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object detection (2)
Change
Detection
Klagenfurt 6-11 April
2011
PAGE 9
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object localization
Klagenfurt 6-11 April
2011
PAGE 10
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object tracking
• It consists in the temporal association of the objects
A'
A B
B'
C
C'
tk
D'
D
D(A,A’) = 2.03
D(A,B’) = 0.39
D(A,B’) = 0.39
D(A,B’) = 0.39
Klagenfurt 6-11 April
A -> A’
D(B,A’) = 0.23
B -> B’
D(B,B’) = 2.0
2011
PAGE 11
D(C,A’) = 0.03
D(C,C’) = 2.39
tk+N
C -> C’
D(D,A’) = 0.0023 D -> D’
D(D,D’) = 4.0
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object tracking (2)
Klagenfurt 6-11 April
2011
PAGE 12
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object classification
PERSON
VEHICLE
GROUP OF
PEOPLE
Neural network
Klagenfurt 6-11 April
2011
PAGE 13
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object classification (2)
Klagenfurt 6-11 April
2011
PAGE 14
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Object tracking and classification
Klagenfurt 6-11 April
2011
PAGE 15
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Event analysis and classification
Event 1
Video
Flow
Features
extraction for
event analysis
Algorithm for
event
classification
Event 2
.
.
.
Event N
Feture Modelling
pattern
Clustering and Timing
• The selected features for event analysis are :
• Tracks
• Starting and ending time
• Class of the object
Klagenfurt 6-11 April
2011
PAGE 16
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Explicit Event Definition
An anomalous event can be
identified on the bases of its
explicit definition.
Person walks
Car 2 exits
Car 1 enters
Klagenfurt 6-11 April
2011
PAGE 17
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Explicit Event Definition (2)
Event 1
Event 2
Event 3
• The sequential recognition of the events 1,2 and 3 could be
interpreted as the accomplishment of an anomalous activity.
• Not always the anomalous events are a simple sequence of
simple events
Klagenfurt 6-11 April
2011
PAGE 18
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
Probabilistic Events Analysis
• The explicit definition of anomalous events is
sometimes to rigorous and it does not fit well
with the dynamics in the scene.
• The explicit definition could be difficult to
perform (either manually or with automatic
learning mechanisms)
Alternative:
• To adopt a probabilistic approach: an unlikely
event with respect to the detected event is also
an anomalous event.
Klagenfurt 6-11 April
2011
PAGE 19
Prof. Micheloni Christian
Università Degli Studi di Udine
Artificial Vision
STATE of the ART
• G.L. Foresti, P. Mahonen and C.S. Regazzoni, Multimedia VideoBased Surveillance Systems: from User Requirements to Research
Solutions, Kluwer Academic Publishers, 2000.
• L. Marcenaro, F. Oberti, G.L. Foresti and C.S. Regazzoni,
“Distributed Architectures and Logical Task Decomposition in
Multimedia Surveillance Systems”, Proceedings of the IEEE, Vol.
89, no. 10, October 2001, pp. 1419-1440.
• G.L. Foresti, C. Micheloni, L. Snidaro, P. Remagnino and T.
Ellis, “Active Video-based Surveillance Systems: the low-level
image and video processing techniques needed for
implementation”, IEEE Signal Processing Magazine, Vol. 22, No.
2, March 2005, pp. 25-37.
Klagenfurt 6-11 April
2011
PAGE 20
Prof. Micheloni Christian
Università Degli Studi di Udine