Vehicle Classification for Facility Monitoring, Los Alamos National Labs, June 2007

 

The goal of the project was to build a low energy, reliable classification system to be deployed at remote access points that can distinguish vehicles into 3 main classes: personal, light weight vehicles such as cars, medium load carrying trucks and heavily loaded trucks. For this project I have designed and implemented a spectral characteristics based classifier using a Stargate / Mica2 pair for processing and communication and acoustic and seismic sensors. The seismic sensor connected to a mica2 with low sampling frequency act as a trip wire which enables the acoustic sensing based classification system on the Stargate. The FLDV based classifier implemented on the Stargate performed with 100% classification accuracy in over 20 runs with each vehicle class. (Detailed slides)

 

Publications

 

1.   J. Frigo, V. Kulathumani, S. Brenna, E. Rosten, E. Raby, Sensor Network Based Vehicle Classification and License Plate identification System, INSS 2009