@conference {18468,
	title = {Multimodal 3-D tracking and event detection via the particle filter},
	booktitle = {IEEE Workshop on Detection and Recognition of Events in Video, 2001. Proceedings},
	year = {2001},
	month = {2001///},
	pages = {20 - 27},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Determining the occurrence of an event is fundamental to developing systems that can observe and react to them. Often, this determination is based on collecting video and/or audio data and determining the state or location of a tracked object. We use Bayesian inference and the particle filter for tracking moving objects, using both video data obtained from multiple cameras and audio data obtained using arrays of microphones. The algorithms developed are applied to determining events arising in two fields of application. In the first, the behavior of a flying echo locating bat as it approaches a moving prey is studied, and the events of search, approach and capture are detected. In a second application we describe detection of turn-taking in a conversation between possibly moving participants recorded using a smart video conferencing setup},
	keywords = {algorithms, APPROACH, audio data collection, audio signal processing, Bayesian inference, Bayesian methods, belief networks, CAMERAS, capture, conversation, echo, Educational institutions, Event detection, event occurrence, filtering theory, flying echo locating bat behaviour, Image motion analysis, inference mechanisms, Laboratories, microphone arrays, moving object tracking, moving participants, moving prey, multimodal 3D tracking, multiple cameras, Object detection, particle filter, Particle filters, Particle tracking, Robustness, search, smart video conferencing setup, target tracking, Teleconferencing, tracking filters, turn-taking detection, video data collection, video signal processing},
	isbn = {0-7695-1293-3},
	doi = {10.1109/EVENT.2001.938862},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {14188,
	title = {A spherical eye from multiple cameras (makes better models of the world)},
	booktitle = {Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001},
	volume = {1},
	year = {2001},
	month = {2001///},
	pages = {I-576- I-583 vol.1 - I-576- I-583 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paper describes an imaging system that has been designed specifically for the purpose of recovering egomotion and structure from video. The system consists of six cameras in a network arranged so that they sample different parts of the visual sphere. This geometric configuration has provable advantages compared to small field of view cameras for the estimation of the system{\textquoteright}s own motion and consequently the estimation of shape models from the individual cameras. The reason is that inherent ambiguities of confusion between translation and rotation disappear. We provide algorithms for the calibration of the system and 3D motion estimation. The calibration is based on a new geometric constraint that relates the images of lines parallel in space to the rotation between the cameras. The 3D motion estimation uses a constraint relating structure directly to image gradients.},
	keywords = {3D motion estimation, Calibration, camera network, CAMERAS, Computer vision, egomotion recovery, geometric configuration, geometric constraint, image gradients, image sampling, imaging system, Laboratories, Layout, Motion estimation, multiple cameras, Pixel, Robot vision systems, SHAPE, shape models, Space technology, spherical eye, system calibration, video, video cameras, video signal processing, visual sphere sampling},
	isbn = {0-7695-1272-0},
	doi = {10.1109/CVPR.2001.990525},
	author = {Baker, P. and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Pless, R.}
}
