DocumentCode :
3016771
Title :
Hierarchical sensor data fusion by probabilistic cue integration for robust 3D object tracking
Author :
Kahler, Olaf ; Denzler, J. ; Triesch, J.
Author_Institution :
Comput. Vision Group, Passau Univ., Germany
fYear :
2004
fDate :
28-30 March 2004
Firstpage :
216
Lastpage :
220
Abstract :
Sensor data fusion from multiple cameras is an important problem for machine vision systems operating in complex, natural environments. We tackle the problem of how information from different sensors can be fused in 3D object tracking. We embed an approach called democratic integration into a probabilistic framework and solve the fusion step by hierarchically fusing the information of different sensors and different information sources (cues) derived from each sensor. We compare different fusion architectures and different adaptation schemes. The experiments for 3D object tracking using three calibrated cameras show that adaptive hierarchical fusion improves the tracking robustness and accuracy compared to a flat fusion strategy.
Keywords :
optical tracking; probability; sensor fusion; video signal processing; 3D object tracking; democratic integration; hierarchical data fusion; hierarchical sensor fusion; machine vision systems; multiple cameras; probabilistic cue integration; surveillance tasks; Airports; Cameras; Cognitive science; Computer vision; Hardware; Machine vision; Robustness; Sensor fusion; Sensor systems; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Analysis and Interpretation, 2004. 6th IEEE Southwest Symposium on
Print_ISBN :
0-7803-8387-7
Type :
conf
DOI :
10.1109/IAI.2004.1300977
Filename :
1300977
Link To Document :
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