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Title: Large scale tracking algorithms

Abstract

Low signal-to-noise 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 multi-hypothesis 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 large-scale tracking in an urban environment.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1177093
Report Number(s):
SAND2015-0209
558307
DOE Contract Number:  
AC04-94AL85000
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. Thu . "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 signal-to-noise 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 multi-hypothesis 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 large-scale tracking in an urban environment.},
doi = {10.2172/1177093},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

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