3D Surveillance A Distributed Network of Smart Cameras for Real-Time Tracking and its Visualization in 3D
- 10 July 2006
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The demand for surveillance systems has increased extremely over recent times. We present a system consisting of a distributed network of cameras that allows for tracking and handover of multiple persons in real time. The intercamera tracking results are embedded as live textures in an integrated 3D world model which is available ubiquitously and can be viewed from arbitrary perspectives independent of the persons’ movements. We mainly concentrate on our implementation of embedded camera nodes in the form of smart cameras and discuss the benefits of such a distributed surveillance network compared to a host centralized approach. We also briefly describe our way of hassle free 3D model acquisition to cover the complete system from setup to operation and finally show some results of both an indoor and an outdoor system in operation.Keywords
This publication has 11 references indexed in Scilit:
- Omnidirectional 3D Modeling on a Mobile Robot using Graph CutsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Adaptive Probabilistic Tracking Embedded in a Smart CameraPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Distributed Interactive Video Arrays for Event Capture and Enhanced Situational AwarenessIEEE Intelligent Systems, 2005
- A Boosted Particle Filter: Multitarget Detection and TrackingLecture Notes in Computer Science, 2004
- Kernel-based object trackingIeee Transactions On Pattern Analysis and Machine Intelligence, 2003
- Counting people in crowds with a real-time network of simple image sensorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Color-Based Probabilistic TrackingLecture Notes in Computer Science, 2002
- Sequential Monte Carlo Methods in PracticePublished by Springer Science and Business Media LLC ,2001
- CONDENSATION—Conditional Density Propagation for Visual TrackingInternational Journal of Computer Vision, 1998
- Empirical methods for the estimation of the mixing probabilities for socially structured populations from a single survey sampleMathematical Population Studies, 1992