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Security Surveillance System

Security Surveillance System . Jeanette Miranda Continuing work by Peter Schiffman and Zac Kelton. Indoor Security Surveillance Systems. Majority single camera systems for indoors Occasional option to add on second camera Remote viewing for IP Cameras Email notifications Digital zoom

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Security Surveillance System

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  1. Security Surveillance System Jeanette Miranda Continuing work by Peter Schiffman and ZacKelton

  2. Indoor Security Surveillance Systems • Majority single camera systems for indoors • Occasional option to add on second camera • Remote viewing for IP Cameras • Email notifications • Digital zoom • Some feature data record only on motion-sensing

  3. Problems • Typical Surveillance System • Limited digital zoom on area of interest • Limited to static view of region. • Mid-low resolution cameras • Want a surveillance system that can: • Obtain high-quality information about the region of the scene where there is motion. • Preferably capture views of the persons face

  4. Project • Design and implement a surveillance system with one stationary camera and one PTZ camera • Detect motion with the stationary camera • Zoom in on area of interest with PTZ camera. • As person continues to move, track their motion with PTZ camera

  5. Building off of Previous Work • ZacKelton and Peter Schiffman • Initially intended to use PTZ cameras, ended up using two stationary cameras (with intent of extending as future project) • Basic background modeling as mixture of Gaussian models • Focused efforts on detection, tracking and camera calibration • Worked with small scale scene rather than large scale scene • Improvements to be made • Replacing one stationary cameras with PTZ camera • Improvements in motion detection

  6. Choosing Cameras • PTZ Camera • IP Camera • Remote controlled pan, tilt and zoom • Optical zoom • Reasonable cost • 640x480 • Stationary Camera • Possibly IP, but not necessary • No pan, tilt, zoom • Camera search still ongoing – talking with Professor Kimia and Anil about different options

  7. Motion Detection • Peter Schiffman and ZacKelton • Initially looked for individual pixel differences between current frame and background. Too sensitive • More robust solution • Represent background image and current frame as single number that is sum of all grayscale pixel values in the image. With • When the difference between the background and the current frame is high, motion in the scene. • Use as starting point. Possible ideas: • Sum rows and columns to help identify where in the scene motion is occurring • Confidence value that motion is occurring, rather than thresholding

  8. Camera Location • Peter Schiffman and ZacKelton • Two images from separate cameras – select similar points on each image and find corresponding homography (invertible transformation in a projective space) • Gives output transformation that relates feature in one image to feature in second image • Used code provided by their TA Ricardo • My approach • Get access to code that they used for homography • Limitations: 2D calibration works best for objects moving closely to the 2D plane. Better for their proposed parking lot setting than for an indoor setting. • Possible alternative: combination of initial camera displacements and motion tracking within PTZ camera to capture motion with second camera

  9. Motion Tracking • Peter Schiffman and ZacKelton • Generate mask for regions of motion through image subtraction • Remove noise using morphological opening (bwareaopen) • Fill gaps in mask • Label the image by regions • Track objects across multiple frames by comparing current position to known previous positions • My approach • Larger emphasis on continuity of foreground across frames • Possibly include facial detection information (from an existing library) in choice of region of scene for PTZ camera to capture

  10. Anticipated Problems Motion Tracking • Tracking objects with same color as background • Tracking multiple objects at same time and choosing one object to zoom in on • Switching between objects if the wrong object is chosen

  11. Schedule • Week 1 • Finalize decision on PTZ camera and purchase • Get access to stationary camera • Successfully stream live stationary camera video feed on computer • Create background images • Week 2 • Motion detection based on image subtraction • Motion tracking of multiple objects • Week 3 • Successfully stream live PTZ camera video feed on computer • Successfully control PTZ remotely • Camera calibration and tracking of single object with PTZ camera • Week 4 • Combining various pieces into a single system and debug

  12. Sources • http://security-cameras-review.toptenreviews.com/frontpoint-security-review.html • http://security-cameras-review.toptenreviews.com/protect-america-review.html • Security Surveillance System Initial Presentation by Peter Schiffman and ZacKelton ENGN161 Fall 2008 • http://www.codeproject.com/Articles/10248/Motion-Detection-Algorithms • Security Surveillance System Presentation 2 by Peter Schiffman and ZacKelton ENGN161 Fall 2008 • Security Surveillance System Presentation 3 by Peter Schiffman and ZacKelton ENGN161 Fall 2008

  13. Sources continued • Evaluating Motion Detection Algorithms: Issues and Resultshttp://dircweb.kingston.ac.uk/papers/Renno_J.R.2006_498196/VS2006.pdf • Adaptive Background Mixture Modelshttp://www.ai.mit.edu/projects/vsam/Publications/stauffer_cvpr98_track.pdf

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