A 3D model based visual surveillance system

This paper presents a visual surveillance system tailored for remote surveillance of indoor environments. It uses a two layers video codec based on a 3D model of the background [2]. The objective is to achieve high data compression rates and to enable the support for applications that require the 3D knowledge of the scene. Examples of such applications are statistical applications (e.g. counting persons) and security applications (e.g. control of entrances in restricted areas). The system supports the ability to localize moving objects in the 3D space assuming that all objects are placed on the ground plane. This feature allows the localization of people on a top view map of the site. Results of some tests are presented in order to show the system ability to deal efficiently with the motion of the camera and to illustrate the two layers separation based on a background/foreground segmentation.

[1]  Peter Eisert,et al.  Motion-based analysis and segmentation of image sequences using 3-D scene models , 1998, Signal Process..

[2]  PeopleIsmail,et al.  W 4 : Who ? When ? Where ? What ? A Real Time System for Detecting and Tracking , 1998 .

[3]  Thomas S. Huang,et al.  Motion and structure from feature correspondences: a review , 1994, Proc. IEEE.

[4]  Choong Woong Lee,et al.  Robust estimation of camera parameters from image sequence for video composition , 1996, Signal Process. Image Commun..

[5]  Gian Luca Foresti A real-time system for video surveillance of unattended outdoor environments , 1998, IEEE Trans. Circuits Syst. Video Technol..

[6]  Fernando Pereira,et al.  The role of analysis in content-based video coding and indexing , 1998, Signal Process..

[7]  Larry S. Davis,et al.  W/sup 4/: Who? When? Where? What? A real time system for detecting and tracking people , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[8]  Luís Corte-Real,et al.  A video coder using 3-D model based background for video surveillance applications , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[9]  Roland Mech,et al.  A noise robust method for segmentation of moving objects in video sequences , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Alessandro Neri,et al.  Automatic moving object and background separation , 1998, Signal Process..

[11]  Rachid Deriche Fast Algorithms for Low-Level Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..