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Security Surveillance System

Security Surveillance System

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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