@article {12606,
	title = {Vehicle Fingerprinting Using Drive-By Sounds},
	year = {2006},
	month = {2006/11//},
	institution = {CENTER FOR AUTOMATION RESEARCH, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {We estimate a vehicle{\textquoteright}s speed, width, and length by jointly estimating its acoustic wave-pattern using a single passive acoustic sensor that records the vehicle{\textquoteright}s drive-by noise. The acoustic wave-pattern is estimated using three envelope shape (ES) components, which approximate the shape of the received signal{\textquoteright}s power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM and number of cylinders to form a vehicle profile vector. This vector provides a compressed statistics that can be used for vehicle identification and classification. Vehicle speed estimation and classification results are provided using field data.},
	keywords = {*ACOUSTIC DETECTORS, *GROUND VEHICLES, *SIGNAL PROCESSING, ACOUSTIC DETECTION AND DETECTORS, AMPLITUDE, Interference, PASSIVE SYSTEMS, spatial distribution, STATISTICAL PROCESSES, SURFACE TRANSPORTATION AND EQUIPMENT, SYMPOSIA, WAVE PROPAGATION},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA481471},
	author = {Cevher, V. and Chellapa, Rama and Gurbuz, A.C. and Shah, F. and McClellan, J.H.}
}
@article {12680,
	title = {Video-Based Automatic Target Recognition},
	year = {2004},
	month = {2004/12//},
	institution = {MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH},
	abstract = {We present an approach for vehicle classification in IR video sequences by integrating detection, tracking and recognition. The method has two steps. First, the moving target is automatically detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using an appearance-model based particle filter. The tracking result is evaluated at each frame. Low confidence in tracking performance initiates a new cycle of detection, tracking and classification. We demonstrate the robustness of the proposed method using outdoor IR video sequences.},
	keywords = {*SEQUENCES(MATHEMATICS), *TARGET RECOGNITION, *VEHICLES, *VIDEO RECORDING, algorithms, AUTOMATIC TRACKING., COMPONENT REPORTS, MOVING TARGETS, SURFACE TRANSPORTATION AND EQUIPMENT, SYMPOSIA, TARGET CLASSIFICATION, TARGET DIRECTION, RANGE AND POSITION FINDING, tracking, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431617},
	author = {Zhou,S. K and Chellapa, Rama and Mei,Xue and Wu,Hao and Qinfen Zheng}
}
