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Respectful Cameras

Jeremy Schiff EECS Department University of California, Berkeley Ken Goldberg, Marci Meingast, Deirdre Mulligan, Pam Samuelson IEOR, EECS, Law University of California, Berkeley http://www.cs.berkeley.edu/~jschiff/RespectfulCameras

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Respectful Cameras

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  1. Jeremy Schiff • EECS Department • University of California, Berkeley • Ken Goldberg, Marci Meingast, • Deirdre Mulligan, Pam Samuelson • IEOR, EECS, Law • University of California, Berkeley http://www.cs.berkeley.edu/~jschiff/RespectfulCameras NSF Science and Technology Center, Team for Research in Ubiquitous Secure Technologies, NSF CCF-0424422, with additional support from Cisco, HP, IBM, Intel, Microsoft, Symmantec, Telecom Italia and United Technologies. Respectful Cameras

  2. Background • New class of Robotic Cameras since 9/11/2001 • $20,000 -> Under $1,000 • Static -> Pan, tilt, zoom (21x) • UK - 3 Million Outdoor Cameras • Now Deploying in Large US Cities Zoom Example

  3. Invasiveness

  4. Objective

  5. Static Marker Detection • Adaboost • Training Phase • Input is data and label • Classifying Phase • Data -> label • Linear function of weak classifiers • Example • Construction Hat Color

  6. 1024313 924116 122528 6020173 6922574 421738 6520978 74220171 4511216 Features • Input from images • Each pixel • red, green, blue (RGB) • Values 0 to 255 • Project into higher dimension • Convert to 9 dimensions • RGB • HSV • Stable over changing lighting • LAB • Good for detecting specularities

  7. Classifiers • Operates on each dimension • Threshold value • Above good and below bad • Above bad and below good • Example

  8. Connected Component • Groups adjacent pixels • Threshold • Minimum Area • Bounding Box • Acceptable Ratio Between Dimensions

  9. Marker Tracking • Particle Filtering • Probabilistic Method for Tracking • Motivates Probabilistic AdaBoost

  10. Particle filters • Non-Parametric • Sample Based Method (Particles) • Particle Density ~ Likelihood • Tracking requires three distributions • Initialization Distribution • Transition Model (Intruder Model) • Observation Model • Determines

  11. Observation Model 0.1 0.1 0.1 0.2 1-p 0.0 0.8 0.6 0.4 p 0.2 0.7 0.9 0.4 p 0.3 0.2 0.1 0.2 0.9 0.9 0.9 0.8 1.0 0.8 0.6 0.6 0.79375 0.8 0.7 0.9 0.6 0.7 0.8 0.9 0.8

  12. Transition Model • State • Position • Bounding-box Width • Bounding-box Height • Orientation • Speed • Add Gaussian Noise to width, height, orientation and speed • Euler Integration to determine new position

  13. Multiple Filters • Single Filter Per Marker • Define overlap • Add Filter when overlap of Static Image Cluster and all filters is below threshold • Delete Filter when prob. of best particle < 0.5 • Delete Filter when 2 filters overlap > threshold

  14. Video – Nearby Hats

  15. Video – Nearby Hats

  16. Video – Lighting

  17. Video – Lighting

  18. Video – Crossing

  19. Video – Crossing

  20. Video – Shirt

  21. Video – Shirt

  22. Future Work • Other Features • Edge Detection • Feature Structure • Generalize to Other Domains • Other Obstruction Mechanisms • Encryption • Full Body • Multiple Cameras

  23. Thank You • Jeremy Schiff: jschiff@cs.berkeley.edu • URL: www.cs.berkeley.edu/~jschiff/RespectfulCameras

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