A Novel Tracking Method Using Filtering and Geometry

Authors: Sang Hoon Lee, Jong Sue Bae, Taewan Kim, Jin Mo Song, Jong Ju Kim

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

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329478

References:

[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.