Distributed localization of networked cameras Export

@proceedings{funiak-etal-ipsn06, abstract = {Camera networks are perhaps the most common type of sensor network and are deployed in a variety of real-world applications including surveillance, intelligent environments and scientific remote monitoring. A key problem in deploying a network of cameras is calibration, i.e., determining the location and orientation of each sensor so that observations in an image can be mapped to locations in the real world. This paper proposes a fully distributed approach for camera network calibration. The cameras collaborate to track an object that moves through the environment and reason probabilistically about which camera poses are consistent with the observed images. This reasoning employs sophisticated techniques for handling the difficult nonlinearities imposed by projective transformations, as well as the dense correlations that arise between distant cameras. Our method requires minimal overlap of the cameras' fields of view and makes very few assumptions about the motion of the object. In contrast to existing approaches, which are centralized, our distributed algorithm scales easily to very large camera networks. We evaluate the system on a real camera network with 25 nodes as well as simulated camera networks of up to 50 cameras and demonstrate that our approach performs well even when communication is lossy.}, author = {Funiak, S. and Guestrin, C. and Paskin, M. and Sukthankar, R.}, citeulike-article-id = {926071}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1662438}, journal = {Information Processing in Sensor Networks, 2006. IPSN 2006. The Fifth International Conference on}, keywords = {distributed, inference, networks, sensor}, pages = {34--42}, posted-at = {2008-03-18 03:43:01}, priority = {2}, title = {Distributed localization of networked cameras}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1662438}, year = {2006} }

TY - CONF ID - funiak-etal-ipsn06 L3 - citeulike-article-id:926071 N2 - Camera networks are perhaps the most common type of sensor network and are deployed in a variety of real-world applications including surveillance, intelligent environments and scientific remote monitoring. A key problem in deploying a network of cameras is calibration, i.e., determining the location and orientation of each sensor so that observations in an image can be mapped to locations in the real world. This paper proposes a fully distributed approach for camera network calibration. The cameras collaborate to track an object that moves through the environment and reason probabilistically about which camera poses are consistent with the observed images. This reasoning employs sophisticated techniques for handling the difficult nonlinearities imposed by projective transformations, as well as the dense correlations that arise between distant cameras. Our method requires minimal overlap of the cameras' fields of view and makes very few assumptions about the motion of the object. In contrast to existing approaches, which are centralized, our distributed algorithm scales easily to very large camera networks. We evaluate the system on a real camera network with 25 nodes as well as simulated camera networks of up to 50 cameras and demonstrate that our approach performs well even when communication is lossy. JF - Information Processing in Sensor Networks, 2006. IPSN 2006. The Fifth International Conference on EP - 42 TI - Distributed localization of networked cameras SP - 34 KW - distributed KW - inference KW - networks KW - sensor AU - Funiak, S AU - Guestrin, C AU - Paskin, M AU - Sukthankar, R PY - 2006/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1662438 ER -