@inbook {913,
	title = {A Bayesian Solution to Robustly Track Multiple Objects from Visual Data},
	booktitle = {INTELLIGENT TECHNIQUES AND TOOLS FOR NOVEL SYSTEM ARCHITECTURES},
	series = {Studies in Computational Intelligence},
	volume = {109},
	year = {2008},
	month = {09/2008},
	pages = {531-547},
	publisher = {Springer-Verlag. },
	organization = {Springer-Verlag. },
	address = {Berlin/Heidelberg (ALEMANIA)},
	abstract = {Different solutions have been proposed for multiple objects tracking based on probabilistic algorithms. In this chapter, the authors propose the use of a single particle filter to track a variable number of objects in a complex environment.
Estimator robustness and adaptability are both increased by the use of a clustering algorithm. Measurements used in the tracking process are extracted from a stereovision system, and thus, the 3D position of the tracked objects is obtained at each time step. As a proof of concept, real results are obtained in a long sequence with a mobile robot moving in a cluttered scene.},
	keywords = {artificial vision, bayesian estimation, Multi-Object Tracking},
	isbn = {978-3-540-77621-5},
	doi = {10.1007/978-3-540-77623-9_30},
	url = {http://www.springer.com/engineering/mathematical/book/978-3-540-77621-5},
	attachments = {https://geintra-uah.org/en/system/files/private/a_bayesian_solution-fulltext.pdf},
	author = {Marta Marron and Garcia, Juan Carlos and Miguel Angel Sotelo and Daniel Pizarro and Ignacio Bravo and Jose Luis Martin}
}
