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VISION for Security
Monique THONNAT
ORIONINRIA Sophia Antipolis
18/03/05 CS
M. Thonnat 2
Which Security Problems? Safety and security of goods and human beings
How? Data captured by video surveillance cameras
Original video understanding approach mixing: computer vision: 4D analysis (3D + temporal analysis)
artificial intelligence: a priori knowledge (scenario, environment)
software engineering: reusable VSIP platform
Introduction
18/03/05 CS
M. Thonnat 3
Definition: real time and automated analysis of video sequences video understanding= from people detection and
tracking to behavior recognition
Recognition of complex behaviors: of individuals (fraud, graffiti, vandalism, bank attack) of small groups (fighting) of crowds (overcrowding) interactions of people and vehicles (aircraft refueling)
Video Understanding for Security
18/03/05 CS
M. Thonnat 4
Video UnderstandingVideo Understanding
4 D analysis:multi-cameras
tracking
Video understanding
People detection
and tracking
Scenario recognition
A PRIORI KNOWLEDGE:• 3d models of the environment • Camera calibration• Scenario Models
Alarms
People detection
and tracking
Interpretation of the videos from pixels to alarms
18/03/05 CS
M. Thonnat 5
Impact: Visual surveillance of metro stations, bank agencies,
trains, buildings and airports 5 European projects (PASSWORDS, AVS-PV, AVS-RTPW,
ADVISOR, AVITRACK)
4 contracts with End-users companies (metro, bank, trains)
2 transfer activities with Bull (Paris) and Vigitec (Brussels)
Cooperation over more than 11 years with partners
Creation of a start-up (spring 2005)
Video Understanding
18/03/05 CS
M. Thonnat 6
Typical problems
Metro station surveillance Surveillance inside trains
Building access control Airport monitoring
18/03/05 CS
M. Thonnat 7
Behavior recognition: approach based on a priori knowledge
model of the empty scene (3D geometry and semantics)
models of predefined scenarios
a language for representing scenarios based on
combination of states and events more than 20 states and 20 events can be used
a reasoning mechanism for real time detection of states,
events and scenarios (e.g. temporal reasoning,
constraints solving techniques)
Video Understanding
18/03/05 CS
M. Thonnat 8
Video Understanding: 3D Scene Model
3d Model of 2 bank agencies
objet du contexte
mur et portezone d’accès
salle du coffrerue
rue
salle automates
zone d’entrée de l’agence
zone des distributeurs
zone de jour/nuit
zone devant le guichet
zone derrière le guichet
zone d’accès au bureau du
directeur
zone de jour
ported’entrée
porte salleautomates
armoire
guichet
commode
Les Hauts de Lagny Villeparisis
18/03/05 CS
M. Thonnat 9
States, Events and Scenarios : State: a spatio-temporal property involving one or several actors on a time interval
Ex : « close», « walking», « seated»
Event: a significant change of states
Ex : « enters», « stands up», « leaves »
Scenario: a long term symbolic application dependent activity
Ex : « fighting», « vandalism»
Video Understanding
18/03/05 CS
M. Thonnat 10
Results for Bank Monitoring
Bank attack scenario description :
scenario Bank_attack_one_robber_one_employeephysical_objects: ((employee : Person), (robber : Person), z1: Back_Counter, z2: Entrance_Zone, z3: Front_Counter, z4: Safe, d: Safe_door)
components: (State c1 : Inside_zone(employee, z1)) (Event c2 : Changes_zone(robber, z2,z3))
(State c3 : Inside_zone(employee, z4)) (State c4 : Inside_zone(robber, z4))) constraints : ((c2 during c1) (c2 before c3) (c1 before c3) (c2 before c4) (c4 during c3) (d is open))
18/03/05 CS
M. Thonnat 11
Video Understanding for bank surveillance
Examples : Brussels and Barcelona Metros
Exit zone
Jumping over barrier
Blocking
Overcrowding
Fighting
Group
behavior
Crowd
behavior
Individual
behavior
Groupbehavior
Results in Metro Surveillance
12
18/03/05 CS
M. Thonnat 13
Video Understanding: Conclusion
Hypotheses: fixed cameras 3D model of the empty scene predefined behavior models
Results: + Behavior understanding for Individuals,
Groups of people, Crowd or Vehicles
+ an operational language for video understanding (more than 20 states and events)
+ a real-time platform (5 to 25 frames/s)
18/03/05 CS
M. Thonnat 14
Knowledge Acquisition Design of ontology driven knowledge acquisition:
video event ontology (T. Van Vu PhD) Design of learning techniques to complement a priori knowledge:
visual concept learning(Nicolas Maillot PhD) scenario model learning (A. Toshev)
Reusability is still an issue for vision programs Use of program supervision techniques: dynamic configuration of
programs and parameters (B Georis PhD)
Video event detection Finer human shape description:3D posture models (B. Boulay PhD) Video analysis robustness: Uncertainty management (M. Zuniga
PhD)
Conclusion: Where we go
18/03/05 CS
M. Thonnat 15
Computer Vision Mobile object detection (Wei Yun I2R Singapore) Tracking of people using geometric approaches (T.
Ellis et al. Kingston University UK)
Event Recognition Probalistic approaches HMM, DBN (A Bobick Georgia
Tech USA, H Buxton Univ Sussex UK)
Reusable platform Realtime video surveillance platform (Multitel, Be)
State of the Art