This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking
February 1996 (vol. 18 no. 2)
pp. 138-150

Abstract—An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the k-best hypotheses are determined in polynomial time using an algorithm due to Murty[24]. The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination, and continuation are demonstrated together with the latter's capability to provide low level support of temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 51 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed, and experimental results show that the algorithm is robust to errors in the motion model. An investigation of the performance of the algorithm as a function of look-ahead (tree depth) indicates that high accuracy can be obtained for tree depths as shallow as three. Experimental results suggest that a real-time MHT solution to the motion correspondence problem is possible for certain classes of scenes.

[1] N. Ayache and O. Faugeras, “Maintaining Representations of the Environment of a Mobile Robot,” IEEE Trans. Robotics and Automation, vol. 5, no. 6, pp. 804-819, 1989.
[2] Y. Bar-Shalom and T.E. Fortmann, Tracking and Data Association. Academic Press, 1988.
[3] W.L. Brogan, "Algorithm for ranked assignments with applications to multiobject tracking," IEEE J. of Guidance, vol. 12, no. 3, pp. 357-364, 1989.
[4] T. Broida, S. Chandrashekhar, and R. Chellappa, "Recursive 3D Motion Estimation From a Monocular Image Sequence," IEEE Trans. Aerospace and Electronic Systems, vol. 26, no. 4, pp. 639-656, 1990
[5] T.J. Broida and R. Chellappa, "Kinematics and structure of a rigid object from a sequence of noisy images," Proc. Workshop on Motion: Representation and Analysis, pp. 95-100, 1986.
[6] Y.L. Chang and J.K. Aggarwal, "3d structure reconstruction from an ego motion sequence using statistical estimation and detection theory," Proc. IEEE Workshop on Visual Motion, pp. 268-273, 1991.
[7] J.B. Collins and J.K. Uhlmann, "Efficient gating in data associaton with multivariate distributed states," IEEE Trans. on Aerospace and Electronic Systems, vol. 28, no. 3, 1992.
[8] I. Cox, “A Review of Statistical Data Association Techniques for Motion Correspondence,” Int'l J. Computer Vision, vol. 10, no. 1, pp. 53-65, 1993.
[9] I.J. Cox and J.J. Leonard, "Probabilistic data association for dynamic world modeling: A multiple hypothesis approach," Proc. Int'l Conf. Advanced Robotics,Pisa, Italy, 1991.
[10] I.J. Cox and J.J. Leonard, "Unsupervised learning for mobile robot navigation using probabilistic data association," Proc. Workshop on Computer Learning and Natural Learning,Berkeley, Calif. 1991.
[11] I.J. Cox, “Modeling a Dynamic Environment Using a Bayesian Multiple Hypothesis Approach,” Artificial Intelligence, vol. 66, no. 2, pp. 311-344, Apr. 1994.
[12] I.J. Cox and M.L. Miller, “On Finding Ranked Assignemnts with Application to Multi-Target Tracking and Motion Correspondence,” AeroSys, vol. 32, no. 1, pp. 486-489, Jan. 1995.
[13] I.J. Cox, M.L. Miller, R. Danchick, and G.E. Newnam, "A comparison of two algorithms for determining ranked assignments with application to multi-target tracking and motion correspondence," Tech. Report, NEC Research Inst., 1995.
[14] I.J. Cox, J.M. Rehg, and S. Hingorani, “A Bayesian Multi-Hypothesis Approach to Edge Grouping and Contour Segmentation,” Int'l J. Computer Vision, vol. 11, no. 1, pp. 5-24, 1993.
[15] R. Danchick and G.E. Newnam, "A fast method for finding the exact N-best hypotheses for multitarget tracking," IEEE Trans. Aerospace and Electronic Systems, vol. 29, no. 2, pp. 555-560, 1993.
[16] M.R.W. Dawson, "The how and why of what went where in apparent motion: Modeling solutions to the motion correspondence problem," Psychological Review, vol. 98, no. 4, pp. 569-603, 1991.
[17] R. Deriche and O. Faugeras,“Tracking line segments,” Proc. First European Conf. Computer Vision, O. Faugeras, ed., pp. 259-268,Antibes, France, Apr. 1990.
[18] T.E. Fortmann, Y. Bar-Shalom, and M. Scheffe, "Sonar tracking of multiple targets using joint probabilistic data association," IEEE J. of Oceanic Engineering, vol. 8, no. 3, pp. 173-184, 1983.
[19] D.W. Jacobs, “Grouping for Recognition,” MIT AI Memo 1177, 1989.
[20] T. Kurien, "Issues in the design of practical multitrget tracking algorithms," Y. Bar-Shalom, ed., Multitarget-Multisensor Tracking: Advanced Applications. pp. 43-83, Artech House, 1990.
[21] B. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," Proc. Seventh Int'l Joint Conf. on Artificial Intelligence, pp. 674-679, 1981.
[22] J.E.W. Mayhew and J.P. Frisby, "Psychophysical and computational studies towards a theory of human stereopsis," Artifical Intelligence, vol. 17, 1981.
[23] F. Meyer and P. Bouthemy, "Region-based tracking in an image sequence," European Conf. on Computer Vision, pp. 476-484, 1992.
[24] K.G. Murty, "An algorithm for ranking all the assignments in order of increasing cost," Operations Research, vol. 16, pp. 682-687, 1968.
[25] V. Nagarajan, M.R. Chideambara, and R.N. Sharma, "Combinatorial problems in multitarget tracking—a comprehensive survey," IEE Proc., Pt F, vol. 134, no. 1, pp. 113-118, 1987.
[26] C.H. Papadimitriu and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. Prentice Hall, 1987.
[27] D. Reid, "An Algorithm for Tracking Multiple Targets," IEEE Trans. Automatic Control, vol. 24, no. 6, pp. 423-432, Dec. 1979.
[28] L.S. Shapiro, H. Wang, and J.M. Brady, "A matching and tracking strategy for independently moving objects," Proc. British Machine Vision Conf., pp. 306-315, 1992.
[29] P. Smith and G. Buechler, "A branching algorithm for discriminating and tracking multiple objects," IEEE Trans. on Automatic Control, vol. 20, pp. 101-104, 1975.
[30] C. Tomasi and T. Kanade, "Shape and Motion From Image Streams Under Orthography: A Factorization Method," Int'l J. Computer Vision, vol. 9, no. 2, pp. 137-154, 1992.
[31] P. Yianilos, “Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces,” Proc. Third Ann. ACM-SIAM Symp. Discrete Algorithms, pp. 311-321, 1993.
[32] Z. Zhang and O.D. Faugeras, "Three-dimensional motion computation and object segmentation in a long sequence of stereo frames," Int'l J. Computer Vision, vol. 7, no. 3, pp. 211-241, 1992.
[33] Q. Zheng and R. Chellappa, "Automatic feature point extraction and tracking in image sequences from unknown camera motion," Proc. Fourth Int'l Conf. on Computer Vision, pp. 335-339, 1993.
[34] Q. Zheng and R. Chellappa, "Automatic feature point extraction and tracking in image sequences for arbitrary camera motion," Int'l J. of Computer Vision. (to be published)
[35] B. Zhou, "Multitarget tracking in clutter: algorithms for data association and state estimation," PhD thesis, Pennsylvania State Univ., 1992.

Index Terms:
Multiple hypothesis tracking, motion correspondence, data association, tracking, visual tracking, ranked bipartite graph matching.
Citation:
Ingemar J. Cox, Sunita L. Hingorani, "An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 2, pp. 138-150, Feb. 1996, doi:10.1109/34.481539
Usage of this product signifies your acceptance of the Terms of Use.