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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| Vydané v: | 2008 International Conference on Computer Science and Software Engineering : 12-14 December 2008 Ročník 6; s. 54 - 57 |
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| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.12.2008
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| ISBN: | 0769533361, 9780769533360 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 surname: Xingke Lian fullname: Xingke Lian organization: Coll. of Inf. Sci. & Eng., Xinjiang Univ., Urumqi – sequence: 2 givenname: A. surname: Hamdulla fullname: Hamdulla, A. 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 |
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