We introduce the first unified theory for target tracking using Multiple Hypothesis Tracking, Topological Data Analysis, and machine learning. Our string of innovations are 1) robust topological features are used to encode behavioral information, 2) statistical models are fitted to distributions over these topological features, and 3) the target type classification methods of Wigren and Bar Shalom et al. are employed to exploit the resulting likelihoods for topological features inside of the tracking procedure. To demonstrate the efficacy of our approach, we test our procedure on synthetic vehicular data generated by the Simulation of Urban Mobility package.
@article{arxiv.1406.0214,
title = {Topological and Statistical Behavior Classifiers for Tracking Applications},
author = {Paul Bendich and Sang Chin and Jesse Clarke and Jonathan deSena and John Harer and Elizabeth Munch and Andrew Newman and David Porter and David Rouse and Nate Strawn and Adam Watkins},
journal= {arXiv preprint arXiv:1406.0214},
year = {2014}
}