Title :
Extended feature-based object tracking in presence of data association uncertainty
Author :
Alvarez, M.S. ; Regazzoni, C.S.
Author_Institution :
Dept. of Biophys. & Electron. Eng., Univ. of Genoa, Genoa, Italy
fDate :
Aug. 30 2011-Sept. 2 2011
Abstract :
This paper proposes and algorithm for extended object tracking using sparse feature points. The described technique is based on the Rao-Blackwellized Particle Filter. In particular, two different data association techniques that take into consideration clutter and missed detections, are coupled and tested in order to provide a comparison of their performance for the problem of extended object tracking.
Keywords :
Monte Carlo methods; object tracking; particle filtering (numerical methods); probability; MCDA; Monte Carlo data asociation; PDAF; RBPF; Rao-blackwellized particle filter; data association uncertainty; extended feature-based object tracking; extended visual object tracking; probabilistic data association filter; sparse feature points; Clutter; Equations; Mathematical model; Shape; Target tracking; Visualization;
Conference_Titel :
Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on
Conference_Location :
Klagenfurt
Print_ISBN :
978-1-4577-0844-2
Electronic_ISBN :
978-1-4577-0843-5
DOI :
10.1109/AVSS.2011.6027308
