Paper
16 April 2008 An approach to target detection in forested scenes
Author Affiliations +
Abstract
Laser-based 3D sensors measure range with high accuracy and allow for detection of several reflecting surfaces for each emitted laser pulse. This makes them particularly suitable for sensing objects behind various types of occlusion, e.g. camouflage nets and tree canopies. Nevertheless, automatic detection and recognition of targets in forested areas is a challenging research problem, especially since foreground objects often cause targets to appear as fragmented. In this paper we propose a sequential approach for detection and recognition of man-made objects in natural forest environments using data from laser-based 3D sensors. First, ground samples and samples too far above the ground (that cannot possibly originate from a target) are identified and removed from further processing. This step typically results in a dramatic data reduction. Possible target samples are then detected using a local flatness criterion, based on the assumption that targets are among the most structured objects in the remaining data. The set of samples is reduced further through shadow analysis, where any possible target locations are found by identifying regions that are occluded by foreground objects. Since we anticipate that targets appear as fragmented, the remaining samples are grouped into a set of larger segments, based on general target characteristics such as maximal dimensions and generic shape. Finally, the segments, each of which corresponds to a target hypothesis, undergo automatic target recognition in order to find the best match from a model library. The approach is evaluated in terms of ROC on real data from scenes in forested areas.
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Christina Grönwall, Tomas Chevalier, Gustav Tolt, and Pierre Andersson "An approach to target detection in forested scenes", Proc. SPIE 6950, Laser Radar Technology and Applications XIII, 69500S (16 April 2008); https://doi.org/10.1117/12.777042
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Cited by 9 scholarly publications and 1 patent.
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KEYWORDS
Target detection

Target recognition

Image segmentation

Sensors

Data modeling

Detection and tracking algorithms

Automatic target recognition

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