Forty Years of Multiple Hypothesis Tracking - A Review of Key Developments

Multiple hypothesis tracking addresses difficult multiple target tracking problems by making association decisions using multiple scans or frames of data. This paper reviews forty years of its development, including the original measurement-oriented approach of Reid, track-oriented approach first formulated by Morefield, distributed processing, and recent graph-based approaches. It also discusses its relationship with random set approaches for tracking.

[1]  Ba-Ngu Vo,et al.  Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter , 2013, IEEE Transactions on Signal Processing.

[2]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[3]  Stefano Coraluppi,et al.  Multi-Stage Multiple-Hypothesis Tracking , 2011, J. Adv. Inf. Fusion.

[4]  Jason L. Williams,et al.  Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Chee-Yee Chong,et al.  Evaluation of Data Association Hypotheses: Non-Poisson I.I.D. Cases , 2004 .

[6]  Ronald P. S. Mahler,et al.  Advances in Statistical Multisource-Multitarget Information Fusion , 2014 .

[7]  D. Castañón Efficient algorithms for finding the K best paths through a trellis , 1990 .

[8]  Lucas Finn,et al.  Multi-target tracklet stitching through network flows , 2011, 2011 Aerospace Conference.

[9]  Wolfgang Koch,et al.  Fixed-interval retrodiction approach to Bayesian IMM-MHT for maneuvering multiple targets , 2000, IEEE Trans. Aerosp. Electron. Syst..

[10]  Chee-Yee Chong,et al.  Graph approaches for data association , 2012, 2012 15th International Conference on Information Fusion.

[11]  Stelios C. A. Thomopoulos,et al.  Distributed Fusion Architectures and Algorithms for Target Tracking , 1997, Proc. IEEE.

[12]  Peter Willett,et al.  Gaussian mixture cardinalized PHD filter for ground moving target tracking , 2007, 2007 10th International Conference on Information Fusion.

[13]  Stefano Coraluppi,et al.  All-Source Track and Identity Fusion , 2000 .

[14]  Chee-Yee Chong,et al.  Generalized Murty's algorithm with application to multiple hypothesis tracking , 2007, 2007 10th International Conference on Information Fusion.

[15]  Alan S. Willsky,et al.  New graph-based and MCMC approaches to multi-INT surveillance , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[16]  Roy L. Streit,et al.  Maximum likelihood method for probabilistic multihypothesis tracking , 1994, Defense, Security, and Sensing.

[17]  Aubrey B. Poore,et al.  Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking , 1994, Comput. Optim. Appl..

[18]  Nathan Cooprider,et al.  Efficient multiple hypothesis tracking by track segment graph , 2009, 2009 12th International Conference on Information Fusion.

[19]  Y. Bar-Shalom,et al.  m-best S-D assignment algorithm with application to multitarget tracking , 2001 .

[20]  Chee-Yee Chong,et al.  Data association hypothesis evaluation for i.i.d. but non-Poisson multiple target tracking , 2004, SPIE Defense + Commercial Sensing.

[21]  Giorgio Battistelli,et al.  Optimal Flow Models for Multiscan Data Association , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Ba-Ngu Vo,et al.  Labeled Random Finite Sets and Multi-Object Conjugate Priors , 2013, IEEE Transactions on Signal Processing.

[23]  C. Morefield Application of 0-1 integer programming to multitarget tracking problems , 1977 .

[24]  S. Mori,et al.  Tracking and classifying multiple targets without a priori identification , 1986 .

[25]  Lingji Chen,et al.  On path cover problems with positive and negative constraints for graph-based multitarget tracking , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[26]  Chee-Yee Chong,et al.  Optimal fusion for non-zero process noise , 2013, Proceedings of the 16th International Conference on Information Fusion.

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

[28]  Yaakov Bar-Shalom,et al.  Dimensionless score function for multiple hypothesis tracking , 2007 .

[29]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[30]  Moe Z. Win,et al.  Message Passing Algorithms for Scalable Multitarget Tracking , 2018, Proceedings of the IEEE.

[31]  Kuo-Chu Chang,et al.  Three formalisms of multiple hypothesis tracking , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[32]  Y. Bar-Shalom,et al.  A generalized S-D assignment algorithm for multisensor-multitarget state estimation , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[33]  Chee-Yee Chong,et al.  Performance analysis of graph-based track stitching , 2013, Proceedings of the 16th International Conference on Information Fusion.

[34]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

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

[36]  Mandar A. Chitre,et al.  The multiple hypothesis tracker derived from finite set statistics , 2017, 2017 20th International Conference on Information Fusion (Fusion).

[37]  David K. Smith Network Flows: Theory, Algorithms, and Applications , 1994 .

[38]  R. Danchick,et al.  Reformulating Reid's MHT method with generalised Murty K-best ranked linear assignment algorithm , 2006 .

[39]  Mandar Chitre,et al.  Relationship Between Finite Set Statistics and the Multiple Hypothesis Tracker , 2018, IEEE Transactions on Aerospace and Electronic Systems.