@conference {13119,
	title = {Decentralized discovery of camera network topology},
	booktitle = {Distributed Smart Cameras, 2008. ICDSC 2008. Second ACM/IEEE International Conference on},
	year = {2008},
	month = {2008/09//},
	pages = {1 - 10},
	abstract = {One of the primary uses of camera networks is the observation and tracking of objects within some domain. Substantial research has gone into tracking objects within single and multiple views. However, few such approaches scale to large numbers of sensors, and those that do require an understanding of the network topology. Camera network topology models camera adjacency in the context of tracking: when an object/entity leaves one camera, which cameras could it appear at next? This paper presents a decentralized approach for estimating a camera networkpsilas topology based on sequential Bayesian estimation using a modified multinomial distribution. Central to this method is an information-theoretic appearance model for observation weighting. The distributed nature of the approach utilizes all of the sensors as processing agents in collectively recovering the network topology. Experimental results are presented using camera networks varying in size from 10-100 nodes.},
	keywords = {adjacency;camera, Bayesian, camera, discovery;modified, distribution;sequential, estimation;Bayes, methods;distributed, multinomial, network, sensors;, sensors;image, topology;decentralized},
	doi = {10.1109/ICDSC.2008.4635696},
	author = {Farrell,R. and Davis, Larry S.}
}
@conference {12594,
	title = {Joint Acoustic-Video Fingerprinting of Vehicles, Part II},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {2},
	year = {2007},
	month = {2007/04//},
	pages = {II-749 -II-752 - II-749 -II-752},
	abstract = {In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data},
	keywords = {acoustic-video, analysis;video, approximations;acoustic, Bayesian, colour, density, efficiency;joint, estimation;Bayes, fingerprinting;metrology, framework;Laplacian, functions;performance, fusion;color, identification;image, invariants;computational, methods;acoustic, metrology;acoustic, processing;, processing;fingerprint, signal, video},
	doi = {10.1109/ICASSP.2007.366344},
	author = {Cevher, V. and Guo, F. and Sankaranarayanan,A. C and Chellapa, Rama}
}
