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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.
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