DocumentCode
2343551
Title
Multiple target tracking using Sequential Monte Carlo Methods and statistical data association
Author
Frank, Oliver ; Nieto, Juan ; Guivant, Jose ; Scheding, Steve
Author_Institution
Swiss Fed. Inst. of Technol. Zurich (ETH), Switzerland
Volume
3
fYear
2003
fDate
27-31 Oct. 2003
Firstpage
2718
Abstract
This paper presents two approaches for the problem of multiple target tracking (MTT) and specifically people tracking. Both filters are based on sequential Monte Carlo methods (SMCM) and joint probability data association (JPDA). The filters have been implemented and tested on real data from a laser measurement system. Experiments show that both approaches are able to track multiple moving persons. A comparison of both filters is given and the advantages and disadvantages of the two approaches are presented.
Keywords
Monte Carlo methods; filtering theory; probability; target tracking; filters; joint probability data association; laser measurement system; multiple moving person tracking; multiple target tracking; people tracking; sequential Monte Carlo methods; statistical data association; Equations; Filters; Monitoring; Noise measurement; Radar tracking; Recursive estimation; State estimation; Target tracking; Time measurement; Vehicle dynamics;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
Print_ISBN
0-7803-7860-1
Type
conf
DOI
10.1109/IROS.2003.1249281
Filename
1249281
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