Vehicle Classification Via 3D Geometries
Abstract
We present a generalized mobile technique which allows for the classification of vehicles by tracking two vehicle based Points of Interest (PoI). Tracking the two PoI allows for the composition of those points into a 3D geometry, which is unique to a given vehicle type. Using high fidelity physics based simulation we demonstrate the capability to classify the 3D geometries in the presence of noise by extracting vector lengths and angles as features. Additionally, we investigate the classification advantages presented by representing the features in multiple linear transform domains and fusing the information from those different domains into a single ensemble classifier.
Publication Date
7-2-2016
Publication Title
Midwest Symposium on Circuits and Systems
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/MWSCAS.2016.7870119
Copyright Status
Unknown
Socpus ID
85015931414 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85015931414
STARS Citation
McDowell, William; Martin, Lockheed; and Mikhael, Wasfy B., "Vehicle Classification Via 3D Geometries" (2016). Scopus Export 2015-2019. 4045.
https://stars.library.ucf.edu/scopus2015/4045