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CG - 3-D Virtual Environments

CG - 3-D Virtual Environments. Reference: “3-D Virtual Environments on Mobile Devices for Remote Surveillance” Vezzani, R.; Cucchiara, R.; Malizia, A.; Cinque, L.; Video and Signal Based Surveillance, 2006. AVSS '06. IEEE International Conference on Nov. 2006

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CG - 3-D Virtual Environments

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  1. CG - 3-D Virtual Environments Reference: “3-D Virtual Environments on Mobile Devices for Remote Surveillance” Vezzani, R.; Cucchiara, R.; Malizia, A.; Cinque, L.; Video and Signal Based Surveillance, 2006. AVSS '06. IEEE International Conference on Nov. 2006 Digital Object Identifier 10.1109/AVSS.2006.2 Name: Yan-Hsiang Wang ID: 94321517

  2. Background (1/2) • Remote surveillance on mobile devices is becoming a wide market demand in many contexts • Home care • Baby monitor

  3. Background (2/2) • Centralized control centers for visual surveillance • management costs much higher than a network of distributed and mobile control points • people employed for watching can’t keep their attention on all the controlled scenes

  4. Limit(1/2) • There is high demand of connections between control centers and distributed mobile platforms to send in real-time surveillance data • The technology of robust video streaming on mobile devices has been improved but sometimes it is unfeasible

  5. Limit(2/2) • It cannot be provided with an acceptable quality due to the unavailability of the connection or the lack of transmission stability • A possible solution is the transcoding of visual data to textual information with a extraction of surveillance knowledge from the videos

  6. Approach(1/2) • Detection of people, moving objects, or suspicious abandoned packs in the scene, the counting of how many people are moving, the estimation of their position and their behavior. • The textual information can be easily transmitted in real-time to mobile devices.

  7. Approach(2/2) • Geometric 3D data on the background scene are pre-loaded in the mobile device • Only the dynamic information is transmitted in real time • The video streams are processed in real-time by local servers, which extract the surveillance knowledge and provide a semantic transcoding for mobile connection

  8. System Architecture • Status • moving • still • Posture • standing • sitting • crawling • laying

  9. Surveillance data extraction • Person occluded by furniture • his detected blob • his appearance image AI • his probability map PM • Blobs belonging to multiple objects are split by the probabilistic tracker

  10. Software components (1/2) • The 3D virtual environments has been built by using the JSR (Java Specification Request) 184 software environments • The software components are based on M3G (Mobile 3D Graphics) library • The M3G file format represents all the objects present inside a three-dimensional scene through the use of a tree structure called scene graph

  11. Software components (2/2) • Tree structure of the 3D virtual environment

  12. 3D virtual environment • 3 points of views • Standard view • Interactive view • Bird eye view • An input frame from a monitored room • Correspondent output of the video surveillance module

  13. Conclusions • This paper presents a distributed video surveillance framework • It’s end is the remote monitoring of the behavior of people moving in a scene exploiting a virtual reconstruction on low capabilities devices • The main novelty of this system is the effective integration of the computer vision and computer graphics modules

  14. Reference • M3G • http://www-128.ibm.com/developerworks/tw/library/wi-mobile1/index.html • http://www.mobilefish.com/developer/m3g/m3g.html • Paper • Probabilistic People Tracking for Occlusion Handling • Detecting Moving Objects, Ghosts, and Shadows in Video Streams

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