Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling

Mohedano del Pozo, Raúl y García Santos, Narciso (2011). Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling. En: "18th IEEE International Conference on Image Processing, ICIP 2011", 11/09/2011 - 14/09/2011, Bruselas, Belgica. ISBN 978-1-4577-1304-0.

Descripción

Título: Simultaneous 3D object tracking and camera parameter estimation by Bayesian methods and transdimensional MCMC sampling
Autor/es:
  • Mohedano del Pozo, Raúl
  • García Santos, Narciso
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 18th IEEE International Conference on Image Processing, ICIP 2011
Fechas del Evento: 11/09/2011 - 14/09/2011
Lugar del Evento: Bruselas, Belgica
Título del Libro: Proceedings of 18th IEEE International Conference on Image Processing, ICIP 2011
Fecha: 2011
ISBN: 978-1-4577-1304-0
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

Multi-camera 3D tracking systems with overlapping cameras represent a powerful mean for scene analysis, as they potentially allow greater robustness than monocular systems and provide useful 3D information about object location and movement. However, their performance relies on accurately calibrated camera networks, which is not a realistic assumption in real surveillance environments. Here, we introduce a multi-camera system for tracking the 3D position of a varying number of objects and simultaneously refin-ing the calibration of the network of overlapping cameras. Therefore, we introduce a Bayesian framework that combines Particle Filtering for tracking with recursive Bayesian estimation methods by means of adapted transdimensional MCMC sampling. Addi-tionally, the system has been designed to work on simple motion detection masks, making it suitable for camera networks with low transmission capabilities. Tests show that our approach allows a successful performance even when starting from clearly inaccurate camera calibrations, which would ruin conventional approaches.

Más información

ID de Registro: 12211
Identificador DC: http://oa.upm.es/12211/
Identificador OAI: oai:oa.upm.es:12211
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6115833
Depositado por: Memoria Investigacion
Depositado el: 29 Ago 2012 08:24
Ultima Modificación: 21 Abr 2016 11:25
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