scholarly journals MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements

2006 ◽  
Vol 28 (12) ◽  
pp. 1960-1972 ◽  
Author(s):  
Z. Khan ◽  
T. Balch ◽  
F. Dellaert
Keyword(s):  
Real Time ◽  
Data Association ◽  
Multitarget Tracking ◽  
Multiple Measurements

scholarly journals The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context

2015 ◽  
Vol 15 (7) ◽  
pp. 88-98
Author(s):  
J. Dezert ◽  
A. Tchamova ◽  
P. Konstantinova
Keyword(s):  
Quality Assessment ◽  
Data Association ◽  
New Method ◽  
Multitarget Tracking ◽  
Belief Functions ◽  
Tracking Systems ◽  
Optimal Assignment ◽  
The Individual ◽  
The Impact

Abstract The main purpose of this paper is to apply and to test the performance of a new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the optimal data association solution for improving the performances of multisensor-multitarget tracking systems. The advantages of its implementation in an illustrative realistic surveillance context, when some of the association decisions are unreliable and doubtful and lead to potentially critical mistake, are discussed. A comparison with the results obtained on the base of Generalized Data Association is made.

scholarly journals Density Based Clustering Data Association Procedure for Real–Time HFSWRs Tracking at OTH Distances

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 39907-39919
Author(s):  
Nikola Stojkovic ◽  
Dejan Nikolic ◽  
Snezana Puzovic
Keyword(s):  
Real Time ◽  
Data Association ◽  
Density Based Clustering ◽  
Clustering Data

MULTIPLE-TARGET CLASSIFICATION AND TRACKING FOR MOBILE ROBOTS USING A 2D LASER RANGE SCANNER

2012 ◽  
Vol 09 (04) ◽  
pp. 1250025 ◽  
Author(s):  
POLYCHRONIS KONDAXAKIS ◽  
HARIS BALTZAKIS
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Moving Objects ◽  
Data Association ◽  
Human Robot Interaction ◽  
Range Data ◽  
Multiple Model ◽  
Target Classification ◽  
Laser Range ◽  
Fuzzy Art

In human–robot interaction developments, detection, tracking and identification of moving objects (DATMO) constitute an important problem. More specifically, in mobile robots this problem becomes harder and more computationally expensive as the environments become dynamic and more densely populated. The problem can be divided into a number of sub-problems, which include the compensation of the robot's motion, measurement clustering, feature extraction, data association, targets' trajectory estimation and finally, target classification. Here, a mobile robot uses 2D laser range data to identify and track moving targets. A Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) tracking algorithm associates the available laser data to track and provide an estimated state vector of targets' position and velocity. Potential moving objects are initially learned in a supervised manner and later on are autonomously classified in real-time using a trained Fuzzy ART neural network classifier. The recognized targets are fed back to the tracker to further improve the track initiation process. The resulting technique introduces a computationally efficient approach to already existing target-tracking and identification research, which is especially suited for real time application scenarios.

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