An efficient implementation and evaluation of Reid's multiple hypothesis tracking algorithm for visual tracking

An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the the k-best hypotheses are determined in polynomial time using an algorithm due to Murty (1968). The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination and continuation are demonstrated. Continuation allows the MHT to function despite temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 60 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.

[1]  Katta G. Murty,et al.  Letter to the Editor - An Algorithm for Ranking all the Assignments in Order of Increasing Cost , 1968, Oper. Res..

[2]  D. Reid.  An algorithm for tracking multiple targets , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[3]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[4]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[5]  D. Jacobs.  Grouping for Recognition , 1989 .

[6]  R. Chellappa,et al.  Recursive 3-D motion estimation from a monocular image sequence , 1990 .

[7]  Olivier Faugeras,et al.  Maintaining representations of the environment of a mobile robot , 1988, IEEE Trans. Robotics Autom..

[8]  J. K. Aggarwal,et al.  3D structure reconstruction from an ego motion sequence using statistical estimation and detection theory , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[9]  Patrick Bouthemy,et al.  Region-Based Tracking in an Image Sequence , 1992, ECCV.

[10]  Larry S. Shapiro,et al.  A Matching and Tracking Strategy for Independently Moving Objects , 1992 .

[11]  J. B. Collins,et al.  Efficient gating in data association with multivariate Gaussian distributed states , 1992 .

[12]  Rama Chellappa,et al.  Automatic feature point extraction and tracking in image sequences for unknown camera motion , 1993, 1993 (4th) International Conference on Computer Vision.

[13]  Ingemar J. Cox,et al.  Modeling a Dynamic Environment Using a Bayesian Multiple Hypothesis Approach , 1994, Artif. Intell..

[14]  Ingemar J. Cox,et al.  On Finding Ranked Assignments With Application to Multi-Target Tracking and Motion Correspondence , 1995 .