A Maximal Fuzzy Entropy Based Gaussian Clustering Algorithm for Tracking Dim Moving Point Targets in Image Sequences

After targetspsila original states were estimated by multi-frame detection method, the tracking windows in which each target may be occur were used to lower the computational load. Then all the observational data could be positioned in a observational matrix, and we used a maximal-entropy Gaussian f...

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Published in:2008 International Conference on Computer Science and Software Engineering : 12-14 December 2008 Vol. 6; pp. 54 - 57
Main Authors: Xingke Lian, Hamdulla, A.
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2008
Subjects:
Clustering algorithms
Clustering methods
difference factor
Entropy
Filters
Image sequences
maximal-entropy gauss fuzzy clustering
measurement matrix
Measurement uncertainty
multi-window fusion
Non-effective measurement
Personal digital assistants
Position measurement
State estimation
Target tracking
ISBN:0769533361, 9780769533360
Online Access:Get full text
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Abstract After targetspsila original states were estimated by multi-frame detection method, the tracking windows in which each target may be occur were used to lower the computational load. Then all the observational data could be positioned in a observational matrix, and we used a maximal-entropy Gaussian fuzzy clustering method to get the membership for each measurements to replace associated probability in traditional PDA filter, then the targetspsila following states were estimated by Kalman filter. This paper gives a new weight distribution scheme for deciding the uncertainty of measurements, and defines maximum effective distance based on difference factor to eliminate non-effective observational data. This method avoids tracking false targets or losing targets when targets are crowded in traditional target-tracking methods, and reduces greatly the computation load and has guaranteed the tracking accuracy.
AbstractList After targetspsila original states were estimated by multi-frame detection method, the tracking windows in which each target may be occur were used to lower the computational load. Then all the observational data could be positioned in a observational matrix, and we used a maximal-entropy Gaussian fuzzy clustering method to get the membership for each measurements to replace associated probability in traditional PDA filter, then the targetspsila following states were estimated by Kalman filter. This paper gives a new weight distribution scheme for deciding the uncertainty of measurements, and defines maximum effective distance based on difference factor to eliminate non-effective observational data. This method avoids tracking false targets or losing targets when targets are crowded in traditional target-tracking methods, and reduces greatly the computation load and has guaranteed the tracking accuracy.
Author Hamdulla, A.
Xingke Lian
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  organization: Coll. of Inf. Sci. & Eng., Xinjiang Univ., Urumqi
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Snippet After targetspsila original states were estimated by multi-frame detection method, the tracking windows in which each target may be occur were used to lower...
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StartPage 54
SubjectTerms Clustering algorithms
Clustering methods
difference factor
Entropy
Filters
Image sequences
maximal-entropy gauss fuzzy clustering
measurement matrix
Measurement uncertainty
multi-window fusion
Non-effective measurement
Personal digital assistants
Position measurement
State estimation
Target tracking
Title A Maximal Fuzzy Entropy Based Gaussian Clustering Algorithm for Tracking Dim Moving Point Targets in Image Sequences
URI https://ieeexplore.ieee.org/document/4723195
Volume 6
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