Multiple sensor multiple object tracking with GMPHD filter

Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tra...

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Bibliographic Details
Published in2007 10th International Conference on Information Fusion pp. 1 - 7
Main Authors Nam Trung Pham, Weimin Huang, Ong, S.H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2007
Subjects
bearing and range tracking
Convergence
Data mining
Filters
Fuses
Gaussian mixture probability hypothesis density
Monte Carlo methods
Particle tracking
Random finite set
Sensor fusion
Sensor phenomena and characterization
Sliding mode control
speaker tracking
State estimation
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Summary:Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tracking. Moreover, it is more reliable and less computational than particle probability hypothesis density filter for multi-sensor multi-object tracking. We demonstrated the efficient of the approach by applications such as bearing and range tracking, and multiple speaker tracking.
DOI:10.1109/ICIF.2007.4408087