In this work, two multitarget trackers - the Cardinalized Probability Hypothesis Density (CPHD) filter and the Recursive Random Sample Consensus (R-RANSAC) algorithm - were applied to three scenarios of the Video Verification of IDentity (VIVID) dataset provided by DARPA. The dataset consists of real video data of multiple cars observed from an unmanned aerial vehicle (UAV) and includes challenging situations such as dense traffic and occlusions. The same detector output was given to each tracker and the same metrics of performance were computed in order to ensure fair comparison of the two tracking approaches. The results show the CPHD did better overall, which was to be expected given that it is the more mature approach.
Target class measurements, if available from automatic target recognition systems, can be incorporated into
multiple target tracking algorithms to improve measurement-to-track association accuracy. In this work, the
performance of the classifier is modeled as a confusion matrix, whose entries are target class likelihood functions
that are used to modify the update equations of the recently derived multiple models CPHD (MMCPHD)
filter. The result is the new classification aided CPHD (CACPHD) filter. Simulations on multistatic sonar datasets
with and without target class measurements show the advantage of including available target class information
into the data association step of the CPHD filter.
In this work, we applied several data compression techniques to simulated data and the Turbofan engine degradation
simulation data set from NASA, with the goal of comparing their performance when coupled with the
Support Vector Machine (SVM) classifier and the SVM regression (SVR) predictor. We consistently attained
correct rates in the neighborhood of 90% for simulated data set, with the Principal Component Analysis (PCA),
Sparse Reconstruction by Separable Approximation (SpaRSA) and Partial Least Squares (PLS) having a slight
edge over the other data reduction methods for data classification. We achieved 22% error rate with SRM for
the Turbofan data set 1 and 40% error rate with PCA for Turbofan data set 2. Throughout the tests we have
performed, PCA proved to be the best data reduction method.
The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target
Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood
of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions
of the PHD filter to the multiple model (MM) framework have been published and were implemented
either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple
model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps
of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case
of a single model, the new MMCPHD equations reduce to the original CPHD equations.
The Gaussian Mixture CardinalizedPHD (GM-CPHD) Tracker was applied to the corrected TNO-Blind dataset,
the SNR adjusted datasets in SEABAR07 and to the Metron dataset generated for the MSTWG (Multistatic
TrackingWorking Group). The increasing difficulty of the datasets is handled by improvements on the tracker.
The tracking results (plots and metrics of performance) are included.