TY - CHAP T1 - A Bayesian Solution to Robustly Track Multiple Objects from Visual Data T2 - INTELLIGENT TECHNIQUES AND TOOLS FOR NOVEL SYSTEM ARCHITECTURES Y1 - 2008 A1 - Marta Marron A1 - Garcia, Juan Carlos A1 - Miguel Angel Sotelo A1 - Daniel Pizarro A1 - Ignacio Bravo A1 - Jose Luis Martin KW - artificial vision KW - bayesian estimation KW - Multi-Object Tracking AB - 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. JF - INTELLIGENT TECHNIQUES AND TOOLS FOR NOVEL SYSTEM ARCHITECTURES T3 - Studies in Computational Intelligence PB - Springer-Verlag. CY - Berlin/Heidelberg (ALEMANIA) VL - 109 SN - 978-3-540-77621-5 UR - http://www.springer.com/engineering/mathematical/book/978-3-540-77621-5 ER -