Computational Optimization and Applications
A New Lagrangian Relaxation Based Algorithm for a Class ofMultidimensional Assignment Problems
Computational Optimization and Applications
Introduction to algorithms
Computational Optimization and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
A study on two measurements-to-tracks data assignment algorithms
Information Sciences: an International Journal
Discrete Applied Mathematics
Tracking clathrin coated pits with a multiple hypothesis based method
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Approximation algorithms for data association problem arising from multitarget tracking
CATS '11 Proceedings of the Seventeenth Computing: The Australasian Theory Symposium - Volume 119
Approximation algorithms for data association problem arising from multitarget tracking
CATS 2011 Proceedings of the Seventeenth Computing on The Australasian Theory Symposium - Volume 119
Bio-inspired optimisation approach for data association in target tracking
International Journal of Wireless and Mobile Computing
A multiple hypothesis based method for particle tracking and its extension for cell segmentation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Multiple target tracking is a subject devoted to the estimation of targets' or objects' states, e.g., position and velocity, over time using a single or multiple sensors. The development of modern tracking systems requires a wide variety of algorithms ranging from gating (preprocessing), state and bias estimation, and development of likelihood ratios to data association. The central problem is the data association problem of partitioning sensor reports into tracks and false alarms. From a data association perspective, multiple target tracking methods divide into two basic classes, single and multiple frame processing. The advantage of multiple frame methods is that current decisions are improved by the ability to change past decisions, making multiple frame methods the choice for difficult tracking problems. The classical multiple frame method that has been well developed is called multiple hypothesis tracking (MHT). In the last ten to fifteen years, a new method, called multiple frame assignments (MFA) has been developed by formulating MHT as a multi-dimensional assignment problem for which modern optimization methods can be utilized in the development of near-optimal solutions for real-time applications. This work reviews a number of the problem formulations, including two-dimensional asymmetric single and multi-assignment problems, the corresponding multi-dimensional versions, and the newer group assignment problems. Some of the current and future needs are also discussed.