Vehicle classification in distributed sensor networks

The task of classifying the types of moving vehicles in a distributed, wireless sensor network is investigated. Specifically, based on an extensive real world experiment, we have compiled a data set that consists of 820 MByte raw time series data, 70 MByte of preprocessed, extracted spectral feature vectors, and baseline classification results using the maximum likelihood classifier. The purpose of this paper is to detail the data collection procedure, the feature extraction and pre-processing steps, and baseline classifier development. The database is available for download at http://www.ece.wisc.edu/(similar to)sensit starting on July 2003. (C) 2004 Elsevier Inc. All rights reserved.