AWASS 2013 Case Study
Computational Self-awareness
in Smart-Camera Networks
Lukas Esterle | LakesideLabs, Klagenfurt Unive...
• Overview of the EPiCS Project
• Surveillance and Camera Networks
• Smart-Cameras
• Multi-Camera Tracking of Objects
• Ch...
The EPiCS Project: Motivation
3
AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
• What are the characteristics of fu...
The EPiCS Project: Motivation
4
AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
• We are increasingly faced with sys...
• Requirements of the application domain can conflict in
functionality, performance, resource usage, costs,
reliability, s...
6
AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
• High performance computing
cluster for financial applications
• ...
Surveillance and Camera Networks
7
AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
Image: http://commons.wikimedia.o...
• Use of Camera Systems
– Traffic monitoring
– Crime prevention
– Crowd control
– Surveillance production lines
– Building...
• Tracking is the process of identifying and locating a (moving)
predefined ‘model’ within consecutive frames of a video.
...
• Tracking is the process of identifying and locating a (moving)
predefined ‘model’ within consecutive frames of a video.
...
• Smart cameras combine processing unit with image sensor
– General purpose smart-cameras from off-the-shelf components
– ...
• Flexible high-performance platform running Linux
– PandaBoardes as main board with USB / CSI / GPMC connected
components...
Multi-Camera Tracking of Objects
15
AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
Camera 1 Camera 2 Camera 3
?
Cam...
Distributed Tracking
16
AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
• Use auctions for exchanging tracking respo...
• On the node level
– How maximise utility while minimise communication
– When and how to advertise objects to other camer...
• Cameras build models/knowledge about the behaviour of the
objects to be tracked
• Cameras derive models/knowledge from t...
• You will be provided:
– The open-source simulation environment CamSim
allowing you to simulate object tracking in multi-...
• During the week, you can expect to do the following:
– Learn about the difficulties in camera coordination for distribut...
• Proficiency in Java
• At least one member in the team with some experience with
machine learning tools and techniques (t...
• HOW?
Compose a self-contained,
short video explaining self-
awareness accessible to a
broad audience
• WHEN?
Submission ...
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Computational Self-awareness in Smart-Camera Networks - Lukas Esterle

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Computational Self-awareness in Smart-Camera Networks - Lukas Esterle

  1. 1. AWASS 2013 Case Study Computational Self-awareness in Smart-Camera Networks Lukas Esterle | LakesideLabs, Klagenfurt University Lukas.Esterle@aau.at Peter R. Lewis | CERCIA, University of Birmingham
  2. 2. • Overview of the EPiCS Project • Surveillance and Camera Networks • Smart-Cameras • Multi-Camera Tracking of Objects • Challenges in Smart-Camera Networks for the week • Self-Awareness in Smart-Camera Networks • Prerequisites 2 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 Outline of this talk
  3. 3. The EPiCS Project: Motivation 3 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 • What are the characteristics of future complex systems? Large Heterogeneous Uncertain Dynamic Decentralised
  4. 4. The EPiCS Project: Motivation 4 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 • We are increasingly faced with systems that are … … and often have conflicting requirements! Large Heterogeneous Uncertain Dynamic Decentralised
  5. 5. • Requirements of the application domain can conflict in functionality, performance, resource usage, costs, reliability, safety and security. • Design systems as collections of self-aware and self-expressive nodes. – Use online learning and adapt to specific scenario – Algorithm selection during runtime focused on goals of node – Interaction between nodes to drive global behaviour. The EPiCS Project: Motivation 5 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  6. 6. 6 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 • High performance computing cluster for financial applications • Multi-camera networks for multi- object tracking • Hypermusic
  7. 7. Surveillance and Camera Networks 7 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 Image: http://commons.wikimedia.org/wiki/File:Three_Surveillance_cameras.jpg up to 4.2 million CCTV cameras in the UK McCahill, Michael, and Clive Norris. "CCTV in Britain." Center for Criminology and Criminal Justice-University of Hull-United Kingdom (2002): 1-70.
  8. 8. • Use of Camera Systems – Traffic monitoring – Crime prevention – Crowd control – Surveillance production lines – Building monitoring – Person & Object tracking Surveillance Cameras 9 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 Image: http://www.fh-wien.ac.at/uploads/pics/080611_Asfinag.JPG
  9. 9. • Tracking is the process of identifying and locating a (moving) predefined ‘model’ within consecutive frames of a video. Person & Object Tracking 10 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  10. 10. • Tracking is the process of identifying and locating a (moving) predefined ‘model’ within consecutive frames of a video. • Drawbacks of using ‘dumb’ cameras – Raw video data – Data has to be transmitted & stored – Data has to be processed – Privacy issues Person & Object Tracking 11 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  11. 11. • Smart cameras combine processing unit with image sensor – General purpose smart-cameras from off-the-shelf components – Special purpose smart-cameras for specific applications • Allows to process images on the camera – Raw image data does not need to leave the camera – Operator only gets relevant and/or aggregated data – Operator only gets information about certain events Smart-Cameras 12 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  12. 12. • Flexible high-performance platform running Linux – PandaBoardes as main board with USB / CSI / GPMC connected components – TI OMAP 4460 processor, 2x ARM Cortex A9 @ 1.2 GHz, 2x Cortex M3 – 1GB DDR2 SDRAM – SD-Card, USB, DVI, HDMI, audio – 802.11 b/g/n – Bluetooth 2.1 + Bluetooth 4 Low Energy – Ubuntu 12.04 Linux • Camera modules – USB – Camera Serial Interface (CSI) – External module connected via GPMC using FPGA Smart-Cameras 13 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  13. 13. Multi-Camera Tracking of Objects 15 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 Camera 1 Camera 2 Camera 3 ? Camera 4 • Which camera to continue tracking? • Identify next camera a priori or use a server or operator. • What about dynamics / uncertainties?
  14. 14. Distributed Tracking 16 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013 • Use auctions for exchanging tracking responsibilities – Cameras act as self-interested agents, i.e., maximize their own utility – Selling camera (currently tracking object) opens the auction – Other cameras return bids with price corresponding to “tracking” confidence – Camera with highest bid continues tracking; trading based on Vickrey auction Bid C4 Camera 1 Camera 2 Camera 3 Camera 4 Init auction Bid C3 Fully distributed approach no a-priori topology knowledge required
  15. 15. • On the node level – How maximise utility while minimise communication – When and how to advertise objects to other cameras – How to value objects within the field of view of a camera • On the network level – How to define good camera strategies to optimise the network-wide outcome Challenges in Multi-Camera Tracking 17 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  16. 16. • Cameras build models/knowledge about the behaviour of the objects to be tracked • Cameras derive models/knowledge from the objects behaviour about themselves • Cameras create models/knowledge about their immediate environment • Cameras derive models/knowledge about their neighbouring cameras Self-awareness in Multi-Camera Systems 18 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  17. 17. • You will be provided: – The open-source simulation environment CamSim allowing you to simulate object tracking in multi-camera networks – Various scenarios for the simulation tool – Matlab scripts for evaluation of your simulation results Main objective for the week: Come up with a distributed way to assign tracking responsibilities in a smart camera network; preserving resources and keeping high tracking utility alike. Self-awareness in Multi-Camera Systems 19 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  18. 18. • During the week, you can expect to do the following: – Learn about the difficulties in camera coordination for distributed tracking – Work with provided strategies for distributed coordination of tracking responsibilities – Build your own scenarios with the provided simulation tool – Come up with your own strategy or modify one of the existing strategies to assign tracking responsibilities in a distributed camera network – Optionally, you may want to relax one of the assumptions made in the simulation tool or come up with your own project for self-awareness in distributed smart-camera networks Self-awareness in Multi-Camera Systems 20 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  19. 19. • Proficiency in Java • At least one member in the team with some experience with machine learning tools and techniques (the more the better). • Knowledge of Matlab is a plus Prerequisites 21 AWASS 2013 Case Study | Lukas Esterle | June 24, 2013
  20. 20. • HOW? Compose a self-contained, short video explaining self- awareness accessible to a broad audience • WHEN? Submission Deadline: July 31, 2013 • DETAILS www.epics-project.eu/contest
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