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Proceedings Paper

Application of feature descriptors to low-pixel-count persistent surveillance tracking systems
Author(s): Jason Edelberg; Christopher Miller; Michael Wilson
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Paper Abstract

The largest challenge with all persistent surveillance systems is they require a trade between area coverage and ground object resolution. This trade typically results in provision of imagery where objects desired to be tracked have a small total number of pixels (often less than a few hundred total). With such low pixel counts, traditional target recognition methods become difficult. For this reason, most persistent surveillance tracking systems are based on detection and tracking of image changes. These change-detection tracking systems, however, struggle to maintain tracks through quick maneuvers, stops, obscurations, and dense traffic. Feature descriptors, including template matching, histogram of oriented gradients (HOG), and local binary patterns (LBP) are evaluated for use in the special case of very low pixel count target detection and track maintenance. These dynamic feature-based detection models are incorporated into a change-detection based tracking system. The resulting composite tracking system will be described as applied to EO and MWIR wide area data collected under a variety of conditions. Resulting tracking system improvements and tradeoffs between feature descriptors are presented.

Paper Details

Date Published: 23 May 2013
PDF: 13 pages
Proc. SPIE 8747, Geospatial InfoFusion III, 87470D (23 May 2013); doi: 10.1117/12.2018502
Show Author Affiliations
Jason Edelberg, Naval Research Lab. (United States)
Christopher Miller, Naval Research Lab. (United States)
Michael Wilson, Naval Research Lab. (United States)


Published in SPIE Proceedings Vol. 8747:
Geospatial InfoFusion III
Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editor(s)

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