Abstract
Computer vision and sensor networks researchers are increasingly motivated to investigate complex multi-camera sensing and control issues that arise in the automatic visual surveillance of extensive, highly populated public spaces such as airports and train stations. However, they often encounter serious impediments to deploying and experimenting with large-scale physical camera networks in such real-world environments. We propose an alternative approach called “Virtual Vision”, which facilitates this type of research through the virtual reality simulation of populated urban spaces, camera sensor networks, and computer vision on commodity computers. We demonstrate the usefulness of our approach by developing two highly automated surveillance systems comprising passive and active pan/tilt/zoom cameras that are deployed in a virtual train station environment populated by autonomous, lifelike virtual pedestrians. The easily reconfigurable virtual cameras distributed in this environment generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring public spaces. The novel multi-camera control strategies that we describe enable the cameras to collaborate in persistently observing pedestrians of interest and in acquiring close-up videos of pedestrians in designated areas.
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Notes
- 1.
With regard to software, a virtual vision simulator consists of an environmental model, character models, an animation engine, and a rendering engine. Most commercial modeling/animation systems enable users to create 3D virtual scenes, including virtual buildings populated by virtual characters, and they incorporate rendering subsystems to illuminate and visualize the scenes. The animation subsystem can animate the virtual characters, but autonomous pedestrian animation is an area of active research in the computer animation community and there are as yet no adequate commercial solutions.
- 2.
We are currently validating our virtual vision paradigm in a collaborative project with the University of California, Riverside, through the development of a virtual vision simulator that emulates a large-scale physical camera network that they have deployed.
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Acknowledgements
We thank Wei Shao for developing and implementing the train station simulator and Mauricio Plaza-Villegas for his valuable contributions. We thank Tom Strat, formerly of DARPA, for his generous support and encouragement.
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Terzopoulos, D., Qureshi, F.Z. (2011). Virtual Vision. In: Bhanu, B., Ravishankar, C., Roy-Chowdhury, A., Aghajan, H., Terzopoulos, D. (eds) Distributed Video Sensor Networks. Springer, London. https://doi.org/10.1007/978-0-85729-127-1_11
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DOI: https://doi.org/10.1007/978-0-85729-127-1_11
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