Feature-aided multiple hypothesis tracking using topological and statistical behavior classifiers

Conference Paper

© 2015 SPIE. 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.

Full Text

Duke Authors

Cited Authors

  • Rouse, D; Watkins, A; Porter, D; Harer, J; Bendich, P; Strawn, N; Munch, E; Desena, J; Clarke, J; Gilbert, J; Chin, S; Newman, A

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 9474 /

Electronic International Standard Serial Number (EISSN)

  • 1996-756X

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9781628415902

Digital Object Identifier (DOI)

  • 10.1117/12.2179555

Citation Source

  • Scopus