Efficiently selecting spatially distributed keypoints for visual tracking

Efficiently selecting spatially distributed keypoints for visual tracking

We describe an algorithm dubbed Suppression via Disk Covering (SDC) to efficiently select a set of strong, spatially distributed key-points, and we show that selecting keypoint in this way significantly improves visual tracking. We also describe two efficient implementation schemes for the popular A...

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Hauptverfasser: Gauglitz, S., Foschini, L., Turk, M., Hollerer, T.
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Conferences
Data structures
Detectors
Robustness
Runtime
Target tracking
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creator Gauglitz, S.
Foschini, L.
Turk, M.
Hollerer, T.
description We describe an algorithm dubbed Suppression via Disk Covering (SDC) to efficiently select a set of strong, spatially distributed key-points, and we show that selecting keypoint in this way significantly improves visual tracking. We also describe two efficient implementation schemes for the popular Adaptive Non-Maximal Suppression algorithm, and show empirically that SDC is significantly faster while providing the same improvements with respect to tracking robustness. In our particular application, using SDC to filter the output of an inexpensive (but, by itself, less reliable) keypoint detector (FAST) results in higher tracking robustness at significantly lower total cost than using a computationally more expensive detector.
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subjects Conferences
Data structures
Detectors
Robustness
Runtime
Target tracking
title Efficiently selecting spatially distributed keypoints for visual tracking
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