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Título: | Robust multiple-people tracking using color-based particle filters |
Autor: | Rowe, Daniel; Huerta, Iván; Gonzàlez, Jordi; Villanueva, Juan J. | Palabras clave: | Pattern recognition: Computer vision Computer vision |
Fecha de publicación: | 2007 | Editor: | Springer Nature | Citación: | Pattern Recognition and Image Analysis: 113-120 (2007) | Serie: | Lecture Notes in Computer Science 4477 | Resumen: | Robust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by means of particle filtering, where occlusions are handled considering the target's predicted trajectories. Model drift is tackled by careful updating, based on the history of likelihood measures. A colour-based likelihood, computed from histogram similarity, is used. Experiments are carried out using sequences from the CAVIAR database. | Descripción: | Presentado al 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA-2007) celebrado en Girona (Spain) del 6 al 8 de junio. | Versión del editor: | http://dx.doi.org/10.1007/978-3-540-72847-4_16 | URI: | http://hdl.handle.net/10261/30355 | DOI: | 10.1007/978-3-540-72847-4_16 | ISBN: | 978-3-540-72846-7 |
Aparece en las colecciones: | (IRII) Libros y partes de libros |
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obust multiple-people.pdf | 1,44 MB | Adobe PDF | Visualizar/Abrir |
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