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Proceedings Paper

Feature fusion using ranking for object tracking in aerial imagery
Author(s): Sema Candemir; Kannappan Palaniappan; Filiz Bunyak; Guna Seetharaman
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Paper Abstract

Aerial wide-area monitoring and tracking using multi-camera arrays poses unique challenges compared to stan- dard full motion video analysis due to low frame rate sampling, accurate registration due to platform motion, low resolution targets, limited image contrast, static and dynamic parallax occlusions.1{3 We have developed a low frame rate tracking system that fuses a rich set of intensity, texture and shape features, which enables adaptation of the tracker to dynamic environment changes and target appearance variabilities. However, improper fusion and overweighting of low quality features can adversely aect target localization and reduce tracking performance. Moreover, the large computational cost associated with extracting a large number of image-based feature sets will in uence tradeos for real-time and on-board tracking. This paper presents a framework for dynamic online ranking-based feature evaluation and fusion in aerial wide-area tracking. We describe a set of ecient descriptors suitable for small sized targets in aerial video based on intensity, texture, and shape feature representations or views. Feature ranking is then used as a selection procedure where target-background discrimination power for each (raw) feature view is scored using a two-class variance ratio approach. A subset of the k-best discriminative features are selected for further processing and fusion. The target match probability or likelihood maps for each of the k features are estimated by comparing target descriptors within a search region using a sliding win- dow approach. The resulting k likelihood maps are fused for target localization using the normalized variance ratio weights. We quantitatively measure the performance of the proposed system using ground-truth tracks within the framework of our tracking evaluation test-bed that incorporates various performance metrics. The proposed feature ranking and fusion approach increases localization accuracy by reducing multimodal eects due to low quality features or background clutter. Adaptive feature ranking increases the robustness of the tracker in dynamically changing environments especially when the object appearance is changing.

Paper Details

Date Published: 22 May 2012
PDF: 9 pages
Proc. SPIE 8396, Geospatial InfoFusion II, 839604 (22 May 2012); doi: 10.1117/12.920529
Show Author Affiliations
Sema Candemir, Univ. of Missouri, Columbia (United States)
Kannappan Palaniappan, Univ. of Missouri, Columbia (United States)
Filiz Bunyak, Univ. of Missouri, Columbia (United States)
Guna Seetharaman, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8396:
Geospatial InfoFusion II
Matthew F. Pellechia; Richard J. Sorensen; Shiloh L. Dockstader; Kannappan Palaniappan; Xuan Liu, Editor(s)

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