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Respectful Cameras

Explore the cutting-edge technology of Respectful Cameras, a new class of robotic surveillance cameras developed since 9/11/2001. These cameras, ranging from $20,000 to under $1,000, offer pan, tilt, zoom capabilities and are now being deployed in large U.S. cities. The system uses strategies like Adaboost, marker tracking with particle filtering, and multiple classifiers operating on various dimensions to enhance surveillance efficiency. Future work includes advancing features like edge detection, encryption, and more. Contact Jeremy Schiff at jschiff@cs.berkeley.edu for further information.

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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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