Multi-scale HOG Prescreening Algorithm for Detection of Buried Explosive Hazards in FL-IR and FL-GPR Data

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 forwardlooking 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.