Design And Analysis Of Modern Tracking Systems

By A. Freud. 2016

1,135 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0536011df741b7b3489b789591bd6102b3f9ff26

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Citations in the corpus

The Labeled Multi-Bernoulli Filter (2014, methodology): “A multi-target system is fundamentally different from a single-target system in that the number of states changes with time due to births and deaths of targets [1]–[3].”
Joint Probabilistic Data Association Revisited (2015, background): “In contrast to the (multiframe) JPDA that maintains the contributions from all potential hypotheses from all tracks, the MHT prunes out invalid hypotheses for each track independently and deleted terms are completely discarded [7], making it impossible to recover from errors.”
Extended Object Tracking: Introduction, Overview and Applications (2016, background): “The Probabilistic Multi-Hypothesis Tracker ( PMHT ) [176] allows more than one measurement per object, and the random matrix model (Section III-A) has been integrated in the PMHT framework, see [200]–[202].”
Will the Pedestrian Cross? A Study on Pedestrian Path Prediction (2014, background): “The IMM-KF is said to be nonsensitive to improperly selected transition probabilities [35].”
Objective comparison of particle tracking methods (2014, methodology): “METHOD 4: PARAMETERS The user parameters of this method (and typical values) are:  Number of iterations of convolution with the Gaussian kernel (10-500).”
Generalized optimal sub-pattern assignment metric (2016, methodology): “This is in accordance with classical performance evaluation methods for MTT [2, Sec.”
Context-Based Pedestrian Path Prediction (2014, methodology): “Kalman filter (KF) [7] as an efficient inference algorithm for such Linear Dynamical Systems (LDS).”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, background): “, [44]), complexity scales very well to very large problems involving well-spaced targets (or small groups of well-spaced targets) since the sub-problems in each cluster are independent.”
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “This ﬁlter is also a (multi-target) tracker when target identities or labels are incorporated into individual target states.”