Large scale tracking algorithms
Abstract
Low signaltonoise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multihypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for largescale tracking in an urban environment.
 Authors:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Publication Date:
 Research Org.:
 Sandia National Lab. (SNLNM), Albuquerque, NM (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1177093
 Report Number(s):
 SAND20150209
558307
 DOE Contract Number:
 AC0494AL85000
 Resource Type:
 Technical Report
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
Citation Formats
Hansen, Ross L., Love, Joshua Alan, Melgaard, David Kennett, Karelitz, David B., Pitts, Todd Alan, Zollweg, Joshua David, Anderson, Dylan Z., Nandy, Prabal, Whitlow, Gary L., Bender, Daniel A., and Byrne, Raymond Harry. Large scale tracking algorithms. United States: N. p., 2015.
Web. doi:10.2172/1177093.
Hansen, Ross L., Love, Joshua Alan, Melgaard, David Kennett, Karelitz, David B., Pitts, Todd Alan, Zollweg, Joshua David, Anderson, Dylan Z., Nandy, Prabal, Whitlow, Gary L., Bender, Daniel A., & Byrne, Raymond Harry. Large scale tracking algorithms. United States. doi:10.2172/1177093.
Hansen, Ross L., Love, Joshua Alan, Melgaard, David Kennett, Karelitz, David B., Pitts, Todd Alan, Zollweg, Joshua David, Anderson, Dylan Z., Nandy, Prabal, Whitlow, Gary L., Bender, Daniel A., and Byrne, Raymond Harry. 2015.
"Large scale tracking algorithms". United States.
doi:10.2172/1177093. https://www.osti.gov/servlets/purl/1177093.
@article{osti_1177093,
title = {Large scale tracking algorithms},
author = {Hansen, Ross L. and Love, Joshua Alan and Melgaard, David Kennett and Karelitz, David B. and Pitts, Todd Alan and Zollweg, Joshua David and Anderson, Dylan Z. and Nandy, Prabal and Whitlow, Gary L. and Bender, Daniel A. and Byrne, Raymond Harry},
abstractNote = {Low signaltonoise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multihypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for largescale tracking in an urban environment.},
doi = {10.2172/1177093},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 1
}

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