3D Tracking Using Multi-view Based Particle Filters

Mohedano del Pozo, Raúl; García Santos, Narciso; Salgado Álvarez de Sotomayor, Luis y Jaureguizar Núñez, Fernando (2009). 3D Tracking Using Multi-view Based Particle Filters. En: "10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2008", 20/10/2008-24/10/2008, Juan-les-Pins, Francia. ISBN 978-3-540-88457-6.

Descripción

Título: 3D Tracking Using Multi-view Based Particle Filters
Autor/es:
  • Mohedano del Pozo, Raúl
  • García Santos, Narciso
  • Salgado Álvarez de Sotomayor, Luis
  • Jaureguizar Núñez, Fernando
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2008
Fechas del Evento: 20/10/2008-24/10/2008
Lugar del Evento: Juan-les-Pins, Francia
Título del Libro: Proceedings of 10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2008
Fecha: 2009
ISBN: 978-3-540-88457-6
Volumen: 5259
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios.

Más información

ID de Registro: 3505
Identificador DC: http://oa.upm.es/3505/
Identificador OAI: oai:oa.upm.es:3505
URL Oficial: http://www.springerlink.com/content/4q647233058j05n5/
Depositado por: Memoria Investigacion
Depositado el: 28 Mar 2011 08:03
Ultima Modificación: 20 Abr 2016 13:03
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