Multi-scale HOG prescreening algorithm for detection of buried explosive hazards in FL-IR and FL-GPR data

K. Stone; J. M. Keller; D. Shaw

doi:10.1117/12.2177663

14 May 2015 Multi-scale HOG prescreening algorithm for detection of buried explosive hazards in FL-IR and FL-GPR data

K. Stone, J. M. Keller, D. Shaw

Proceedings Volume 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX; 94540Q (2015) https://doi.org/10.1117/12.2177663
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

A sliding window based prescreening algorithm, utilizing multi-scale histogram of oriented gradient (MS-HOG) features and a linear support vector machine (SVM) classifier, for detection of buried explosive hazards in forward-looking infrared (FL-IR) and forward-looking ground penetrating radar (FL-GPR) data is presented. This algorithm is compared to previously published FL-IR and FL-GPR prescreening algorithms. The MS-HOG prescreening approach has higher computational complexity, but improves overall detection rates, especially for low-contrast and obscured target signatures. Results are presented on several data sets collected at US Army test sites. These collections span several days, and the FL-IR collections include imagery from both long-wave and mid-wave infrared cameras at multiple standoff distances captured at different hours of the day and different times of the year.

Citation Download Citation

K. Stone, J. M. Keller, and D. Shaw "Multi-scale HOG prescreening algorithm for detection of buried explosive hazards in FL-IR and FL-GPR data", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94540Q (14 May 2015); https://doi.org/10.1117/12.2177663

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