@article {14506,
	title = {Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks},
	year = {2008},
	month = {2008/06/29/},
	institution = {OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Our work has focused on developing new cost sensitive feature acquisition and classification algorithms, mapping these algorithms onto camera networks, and creating a test bed of video data and implemented vision algorithms that we can use to implement these. First, we will describe a new algorithm that we have developed for feature acquisition in Hidden Markov Models (HMMs). This is particularly useful for inference tasks involving video from a single camera, in which the relationship between frames of video can be modeled as a Markov chain. We describe this algorithm in the context of using background subtraction results to identify portions of video that contain a moving object. Next, we will describe new algorithms that apply to general graphical models. These can be tested using existing test sets that are drawn from a range of domains in addition to sensor networks.},
	keywords = {*ALGORITHMS, *CLASSIFICATION, data acquisition, DATA FUSION, Detectors, Feature extraction, HMM(HIDDEN MARKOV MODELS), NETWORKS, NUMERICAL MATHEMATICS, PE611102, RANDOM FIELDS, STATISTICS AND PROBABILITY, TEST SETS, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA500520},
	author = {Jacobs, David W. and Getoor, Lise}
}
@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}
}
