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Proceedings Paper

A benchmark for vehicle detection on wide area motion imagery
Author(s): Joseph Catrambone; Ismail Amzovski; Pengpeng Liang; Erik Blasch; Carolyn Sheaff; Zhonghai Wang; Genshe Chen; Haibin Ling
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Paper Abstract

Wide area motion imagery (WAMI) has been attracting an increased amount of research attention due to its large spatial and temporal coverage. An important application includes moving target analysis, where vehicle detection is often one of the first steps before advanced activity analysis. While there exist many vehicle detection algorithms, a thorough evaluation of them on WAMI data still remains a challenge mainly due to the lack of an appropriate benchmark data set. In this paper, we address a research need by presenting a new benchmark for wide area motion imagery vehicle detection data. The WAMI benchmark is based on the recently available Wright-Patterson Air Force Base (WPAFB09) dataset and the Temple Resolved Uncertainty Target History (TRUTH) associated target annotation. Trajectory annotations were provided in the original release of the WPAFB09 dataset, but detailed vehicle annotations were not available with the dataset. In addition, annotations of static vehicles, e.g., in parking lots, are also not identified in the original release. Addressing these issues, we re-annotated the whole dataset with detailed information for each vehicle, including not only a target’s location, but also its pose and size. The annotated WAMI data set should be useful to community for a common benchmark to compare WAMI detection, tracking, and identification methods.

Paper Details

Date Published: 22 May 2015
PDF: 7 pages
Proc. SPIE 9469, Sensors and Systems for Space Applications VIII, 94690F (22 May 2015); doi: 10.1117/12.2178535
Show Author Affiliations
Joseph Catrambone, Temple Univ. (United States)
Ismail Amzovski, Temple Univ. (United States)
Pengpeng Liang, Temple Univ. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Carolyn Sheaff, Air Force Research Lab. (United States)
Zhonghai Wang, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Haibin Ling, Temple Univ. (United States)

Published in SPIE Proceedings Vol. 9469:
Sensors and Systems for Space Applications VIII
Khanh D. Pham; Genshe Chen, Editor(s)

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