Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers
David Rouse, Adam S. Watkins, David William Porter, John Harer, Paul Bendich, Nate Strawn, Elizabeth Munch, Jonathan T. DeSena, Jesse C. Clarke, Jeffrey Gilbert, Peter S. Chin, Andrew J. Newman · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
This paper introduces a method to integrate target behavior into the multiple hypothesis tracker (MHT) likelihood ratio. In particular, a periodic track appraisal based on behavior is introduced that uses elementary topological data analysis coupled with basic machine learning techniques. The track appraisal adjusts the traditional kinematic data association likelihood (i.e., track score) using an established formulation for classification-aided data association. The proposed method is tested and demonstrated on synthetic vehicular data representing an urban traffic scene generated by the Simulation of Urban Mobility package. The vehicles in the scene exhibit different driving behaviors. The proposed method distinguishes those behaviors and shows improved data association decisions relative to a conventional, kinematic MHT.