Establishing motion correspondence using extended temporal scope

Artificial Intelligence

Volumn 145, Issue 1-2, 2003, Pages 227-243

  Veenman, C J ^a   Reinders, M J T ^a   Backer, E ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Computer vision; Feature point tracking; Motion correspondence; Multi dimensional assignment problem; Multi frame optimization; Multi target tracking

Indexed keywords

COMPUTER VISION; IMAGE PROCESSING; INFORMATION ANALYSIS; OPTIMIZATION; PROBLEM SOLVING;

FEATURE POINT TRACKING;

ARTIFICIAL INTELLIGENCE;

EID: 0037380387     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(02)00380-6     Document Type: Article

References (38)

1. Bandelt H.-J., Crama Y., Spieksma F.C.R. Approximation algorithms for multi-dimensional assignment problems with decomposable costs. Discrete Appl. Math. 49:1994;25-50.
2. Bellmore M., Malone J.C. Pathology of traveling-salesman subtour-elimination algorithms. Oper. Res. 19:1971;278-307.
3. Chetverikov D., Verestóy J. Feature point tracking for incomplete trajectories. Computing (Devoted Issue on Digital Image Processing). 62:1999;321-338.
4. Cox I.J. A review of statistical data association techniques for motion correspondence. Internat. J. Comput. Vision. 10:(1):1993;53-66.
5. Cox I.J., Hingorani S.L. An efficient implementation of Reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Machine Intelligence. 18:(2):1996;138-150.
6. Cox I.J., Miller M.L. On finding ranked assignments with applications to multi-target tracking and motion correspondence. IEEE Trans. Aerospace and Electronic Systems. 32:(1):1995;486-489.
7. Cox I.J., Miller M.L., Danchick R., Newnam G.E. A comparison of two algorithms for determining ranked assignments with application to multi-target tracking and motion correspondence. IEEE Trans. Aerospace and Electronic Systems. 33:(1):1997;295-300.
8. Danchick R., Newnam G.E. A fast method for finding the exact N-best hypotheses for multitarget tracking. IEEE Trans. Aerospace and Electronic Systems. 29:(2):1993;555-560.
9. Deb S., Pattipati K.R., Bar-Shalom Y. A new algorithm for the generalized multidimensional assignment problem. IEEE International Conference on Systems, Man and Cybernetics; Emergent Innovations in Information Transfer Processing and Decision Making. 1992;249-254.
10. Deb S., Yeddanapudi M., Pattipati K., Bar-Shalom Y. A generalized S-D assignment algorithm for multisensor-multitarget state estimation. IEEE Trans. Aerospace and Electronic Systems. 33:(2):1997;523-538.
11. Fortmann T.E., Bar-Shalom Y., Sheffe M. Sonar tracking of multiple targets using joint probabilistic data association. IEEE J. Oceanic Engineering. 8:(3):1983;173-184.
12. Horn B.K.P., Schunck B.G. Determining optical flow. Artificial Intelligence. 17:1981;185-203.
13. Hwang V.S.S. Tracking feature points in time-varying images using an opportunistic selection approach. Pattern Recognition. 22:(3):1989;247-256.
14. Karp R.M. Reducibility among combinatorial problems. Miller R.E., Thatcher J.W. Complexity of Computer Computations. 1972;85-103 Plenum Press, New York.
15. Kuhn H.W. The hungarian method for solving the assignment problem. Naval Research Logistics Quarterly. 2:1955;83-97.
16. Miller M.L., Stone H.S., Cox I.J. Optimizing Murty's ranked assignment method. IEEE Trans. Aerospace and Electronic Systems. 33:(3):1997;851-861.
17. Murty K.G. An algorithm for ranking all the assignments in order of increasing cost. Oper. Res. 16:1968;682-687.
18. Nagarajan V., Chidambara M.R., Sharma R.N. Combinatorial problems in multitarget tracking - A comprehensive solution. IEE Proc. 134:(1):1987;113-118.
19. Nagel H.H. Displacement vectors derived from second order intensity variations in image sequences. Computer Vision Graphics and Image Processing. 21:(1):1983;85-117.
20. Pless R., Brodský T., Aloimonos Y. Detecting independent motion: The statistics of temporal continuity. IEEE Trans. Pattern Anal. Machine Intelligence. 22:(8):2000;768-773.
21. Poore A.B. Multidimensional assignments and multitarget tracking. Partitioning Data Sets; DIMACS Workshop, New Brunswick, USA. 1995;169-196 American Mathematical Society, Providence, RI.
22. Poore A.B., Yan X. Data association in multi-frame processing. Proceedings of the Second International Conference on Information Fusion, Vol. II, Sunnyvale, USA. 1999;1037-1044.
23. Rangarajan K., Sha M. Establishing motion correspondence. CVGIP: Image Understanding. 24:(6):1991;56-73.
24. Reid D.B. An algorithm for tracking multiple targets. IEEE Trans. Automat. Control. 24:(6):1979;843-854.
25. Roach J.W., Aggarwal J.K. Determining the movement of objects from a sequence of images. IEEE Trans. Pattern Anal. Machine Intelligence. 2:(6):1980;554-562.
26. Salari V., Sethi I.K. Feature point correspondence in the presence of occlusion. IEEE Trans. Pattern Anal. Machine Intelligence. 12:(1):1990;91-97.
27. Sethi I.K., Jain R. Finding trajectories of feature points in a monocular image sequence. IEEE Trans. Pattern Anal. Machine Intelligence. 9:(1):1987;56-73.
28. Shea P.J., Poore A.B. Computational experiences with hot starts for a moving window implementation of track maintenance. Proceedings of the SPIE: The International Society for Optical Engineering. 3373:1998;428-439.
29. Smith P., Buechler G. A branching algorithm for discriminating and tracking multiple objects. IEEE Trans. Automatic Control. 20:1975;101-104.
30. Stauffer C., Grimson W.E.L. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Machine Intelligence. 22:(8):2000;747-757.
31. Tsai R.Y., Huang T.S. Estimating three dimensional motion parameters of a rigid planar patch, III: Finite point correspondences and the three view problem. IEEE Trans. Acoustics, Speech and Signal Processing. 32:1984;213-220.
32. Ullman S. The Interpretation of Visual Motion. 1979;MIT Press, Cambridge, MA.
33. Veenman C.J., Reinders M.J.T., Backer E. Resolving motion correspondence for densely moving points. IEEE Trans. Pattern Anal. Machine Intelligence. 23:(1):2001;54-72.
34. Verestóy J., Chetverikov D. Experimental comparative evaluation of feature point tracking algorithms. Proceedings Workshop on Evaluation and Validation of Computer Vision Algorithms. Computational Imaging and Vision. 2000;183-194 Kluwer, Dordrecht.
35. Webb J.A., Aggarwal J.K. Structure from motion from rigid and jointed objects. Artificial Intelligence. 19:1982;107-130.
36. Wixson L. Detecting salient motion by accumulating directionally-consistent flow. IEEE Trans. Pattern Anal. Machine Intelligence. 22:(8):2000;774-780.
37. Zhang Z. Estimating motion and structure from correspondences of line segments between two perspective images. IEEE Trans. Pattern Anal. Machine Intelligence. 17:(12):1995;1129-1139.
38. Zhang Z., Faugeras O. Three-dimensional motion computation and object segmentation in a long sequence of stereo frames. Internat. J. Computer Vision. 7:(3):1992;211-241.