Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm

Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements provided by one or multiple sensors. Additional information, such as imperfect estimates of target classes provided by a classifier, can facilitate the target-measurement association and thus improve MTT performance. In this letter, we describe how a recently proposed MTT framework based on the sum-product algorithm can be extended to efficiently exploit class information. The effectiveness of the proposed approach is demonstrated by simulation results.

[1]  H. Vincent Poor,et al.  An introduction to signal detection and estimation (2nd ed.) , 1994 .

[2]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[3]  Brendan J. Frey,et al.  Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.

[4]  Oliver E. Drummond,et al.  Feature, attribute, and classification aided target tracking , 2001 .

[5]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[6]  H.-A. Loeliger,et al.  An introduction to factor graphs , 2004, IEEE Signal Process. Mag..

[7]  Y. Bar-Shalom,et al.  Tracking with classification-aided multiframe data association , 2003, IEEE Transactions on Aerospace and Electronic Systems.

[8]  A. Doucet,et al.  Sequential Monte Carlo methods for multitarget filtering with random finite sets , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[9]  S. Godsill,et al.  Monte Carlo filtering for multi target tracking and data association , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Li Ping,et al.  The Factor Graph Approach to Model-Based Signal Processing , 2007, Proceedings of the IEEE.

[11]  Krishna R. Pattipati,et al.  Anomaly Detection via Feature-Aided Tracking and Hidden Markov Models , 2007, 2007 IEEE Aerospace Conference.

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

[13]  Ba-Ngu Vo,et al.  Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter , 2007, IEEE Transactions on Signal Processing.

[14]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.

[15]  M. Bayati,et al.  Max-Product for Maximum Weight Matching: Convergence, Correctness, and LP Duality , 2008, IEEE Transactions on Information Theory.

[16]  Yaakov Bar-Shalom,et al.  Feature-aided localization of ground vehicles using passive acoustic sensor arrays , 2009, 2009 12th International Conference on Information Fusion.

[17]  Branko Ristic,et al.  A Metric for Performance Evaluation of Multi-Target Tracking Algorithms , 2011, IEEE Transactions on Signal Processing.

[18]  Wen Shuliang,et al.  Feature aided Gaussian mixture probability hypothesis density filter with modified 2D assignment , 2011, Proceedings of 2011 IEEE CIE International Conference on Radar.

[19]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Giorgio Battistelli,et al.  Robust Multisensor Multitarget Tracker with Application to Passive Multistatic Radar Tracking , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Peter Willett,et al.  The GMCPHD tracker applied to the Clutter09 dataset , 2013, Proceedings of the 16th International Conference on Information Fusion.

[22]  Garfield R. Mellema.  Feature-aided tracking in dense clutter using the Clutter09 data set , 2014, 17th International Conference on Information Fusion (FUSION).

[23]  Jason L. Williams,et al.  Approximate evaluation of marginal association probabilities with belief propagation , 2012, IEEE Transactions on Aerospace and Electronic Systems.

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

[25]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Kuo-Chu Chang,et al.  Performance prediction of feature-aided track-to-track association , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[27]  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.

[28]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[29]  Michael G. Rabbat,et al.  Multisensor CPHD filter , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[30]  Paolo Braca,et al.  A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors , 2016, IEEE Transactions on Signal Processing.

[31]  Y. Bar-Shalom,et al.  Tracking And Data Fusion A Handbook Of Algorithms By | , 2017 .

[32]  Ángel F. García-Fernández,et al.  Generalized optimal sub-pattern assignment metric , 2016, 2017 20th International Conference on Information Fusion (Fusion).

[33]  Karl Granström,et al.  Performance evaluation of multi-bernoulli conjugate priors for multi-target filtering , 2017, 2017 20th International Conference on Information Fusion (Fusion).

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

[35]  Ángel F. García-Fernández,et al.  Poisson Multi-Bernoulli Mixture Trackers: Continuity Through Random Finite Sets of Trajectories , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[36]  Ángel F. García-Fernández,et al.  Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[37]  Paolo Braca,et al.  Belief Propagation Based AIS/Radar Data Fusion for Multi - Target Tracking , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[38]  Pramod K. Varshney,et al.  Decentralized Gaussian Filters for Cooperative Self-Localization and Multi-Target Tracking , 2019, IEEE Transactions on Signal Processing.

[39]  Moe Z. Win,et al.  Heterogeneous Information Fusion for Multitarget Tracking Using the Sum-product Algorithm , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[40]  Paolo Braca,et al.  Self-Tuning Algorithms for Multisensor-Multitarget Tracking Using Belief Propagation , 2019, IEEE Transactions on Signal Processing.

[41]  Q. Pan,et al.  On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking , 2020, IEEE Transactions on Signal Processing.