A Novel Tracking Method Using Filtering and Geometry
Image target detection and tracking methods based on
target information such as intensity, shape model, histogram and
target dynamics have been proven to be robust to target model
variations and background clutters as shown by recent researches.
However, no definitive answer has been given to occluded target by
counter measure or limited field of view(FOV). In this paper, we
will present a novel tracking method using filtering and computational
geometry. This paper has two central goals: 1) to deal with vulnerable
target measurements; and 2) to maintain target tracking out of FOV
using non-target-originated information. The experimental results,
obtained with airborne images, show a robust tracking ability with
respect to the existing approaches. In exploring the questions of target
tracking, this paper will be limited to consideration of airborne image.
[1] Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer. Kernel-based
object tracking. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 25(5):564-575, 2003.
[2] X.R. Li and Y. Bar-Shalom. Tracking in clutter with nearest neighbor
filters: analysis and performance. IEEE transactions on aerospace and
electronic systems, 32(3):995-1010, 1996.
[3] X.R. Li and X. Zhi. PSNF: A refined strongest neighbor filter for
tracking in clutter. In IEEE CONFERENCE ON DECISION AND
CONTROL, volume 3, pages 2557-2562. INSTITUTE OF ELECTRICAL
ENGINEERS INC (IEE), 1996.
[4] D.G. Lowe. Distinctive image features from scale-invariant keypoints.
International journal of computer vision, 60(2):91-110, 2004.
[5] R. Hartley and A. Zisserman. Multiple view geometry. Cambridge
university press, 2000.
[6] G.H. Golub and C. Reinsch. Singular value decomposition and least
squares solutions. Numerische Mathematik, 14(5):403-420, 1970.
[7] C. Harris and M. Stephens. A combined corner and edge detector. In
Alvey vision conference, volume 15, page 50. Manchester, UK, 1988.
[8] M.A. Fischler and R.C. Bolles. Random sample consensus: A paradigm
for model fitting with applications to image analysis and automated
cartography. Communications of the ACM, 24(6):381-395, 1981.
[9] J. S. Bae, S. H. Lee, Y. Kim, and Y. S. Jung. An Imaging Target Tracking
Software for a Precision Guided Missile Application. In Proc. Thirteenth
International Conference on Information Fusion, 2010.
[10] K.J. Rhee, D.G. Lee, and T.L. Song. A Probabilistic Strongest Neighbor
Filter Algorithm for m Validated Measurements. In Fusion 2004:
Seventh International Conference on Information Fusion; Stockholm.
International Society of Information Fusion, ONERA-DTIM, BP 72, 29
Av. de la Division Leclerc, Chatillon, 92320, France,, 2004.
[11] T.L. Song and D.S. Kim. Highest Probability Data Association for
Active Sonar Tracking. In Information Fusion, 2006 9th International
Conference on, pages 1-8, 2006.
[1] Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer. Kernel-based
object tracking. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 25(5):564-575, 2003.
[2] X.R. Li and Y. Bar-Shalom. Tracking in clutter with nearest neighbor
filters: analysis and performance. IEEE transactions on aerospace and
electronic systems, 32(3):995-1010, 1996.
[3] X.R. Li and X. Zhi. PSNF: A refined strongest neighbor filter for
tracking in clutter. In IEEE CONFERENCE ON DECISION AND
CONTROL, volume 3, pages 2557-2562. INSTITUTE OF ELECTRICAL
ENGINEERS INC (IEE), 1996.
[4] D.G. Lowe. Distinctive image features from scale-invariant keypoints.
International journal of computer vision, 60(2):91-110, 2004.
[5] R. Hartley and A. Zisserman. Multiple view geometry. Cambridge
university press, 2000.
[6] G.H. Golub and C. Reinsch. Singular value decomposition and least
squares solutions. Numerische Mathematik, 14(5):403-420, 1970.
[7] C. Harris and M. Stephens. A combined corner and edge detector. In
Alvey vision conference, volume 15, page 50. Manchester, UK, 1988.
[8] M.A. Fischler and R.C. Bolles. Random sample consensus: A paradigm
for model fitting with applications to image analysis and automated
cartography. Communications of the ACM, 24(6):381-395, 1981.
[9] J. S. Bae, S. H. Lee, Y. Kim, and Y. S. Jung. An Imaging Target Tracking
Software for a Precision Guided Missile Application. In Proc. Thirteenth
International Conference on Information Fusion, 2010.
[10] K.J. Rhee, D.G. Lee, and T.L. Song. A Probabilistic Strongest Neighbor
Filter Algorithm for m Validated Measurements. In Fusion 2004:
Seventh International Conference on Information Fusion; Stockholm.
International Society of Information Fusion, ONERA-DTIM, BP 72, 29
Av. de la Division Leclerc, Chatillon, 92320, France,, 2004.
[11] T.L. Song and D.S. Kim. Highest Probability Data Association for
Active Sonar Tracking. In Information Fusion, 2006 9th International
Conference on, pages 1-8, 2006.
@article{"International Journal of Electrical, Electronic and Communication Sciences:50149", author = "Sang Hoon Lee and Jong Sue Bae and Taewan Kim and Jin Mo Song and Jong Ju Kim", title = "A Novel Tracking Method Using Filtering and Geometry", abstract = "Image target detection and tracking methods based on
target information such as intensity, shape model, histogram and
target dynamics have been proven to be robust to target model
variations and background clutters as shown by recent researches.
However, no definitive answer has been given to occluded target by
counter measure or limited field of view(FOV). In this paper, we
will present a novel tracking method using filtering and computational
geometry. This paper has two central goals: 1) to deal with vulnerable
target measurements; and 2) to maintain target tracking out of FOV
using non-target-originated information. The experimental results,
obtained with airborne images, show a robust tracking ability with
respect to the existing approaches. In exploring the questions of target
tracking, this paper will be limited to consideration of airborne image.", keywords = "Tracking, Computational geometry, Homography, Filter", volume = "5", number = "11", pages = "1404-7", }