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Geometric Cross-Modal Comparison of Heterogeneous Sensor Data by Tralie, Christopher J.; Smith, Abraham; Borggren, Nathan; Hineman, Jay; Bendich, Paul; Zulch, Peter; Harer, John is a scholarly article available to read on EtoBox.

What is Geometric Cross-Modal Comparison of Heterogeneous Sensor Data about?

In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at various locations. The information represented by the two modalities is compared using self-similarity matrices (SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. Several metrics for comparing SSMs are explored, including a cutting-edge time-warping technique that can simultaneously handle local time warping and partial matches, while also controlling for the change in geometry between feature spaces of the two modalities. We note that this technique is quite general, and does not depend on the choice of modalities. In this particular setting, we demonstrate that the cross-modal distance between SSMs corresponding to the same trajectory type is smaller than the cross-modal distance between SSMs corresponding to distinct trajectory types, and we formalize this observation via precision-recall metric

Author

Tralie, Christopher J.; Smith, Abraham; Borggren, Nathan; Hineman, Jay; Bendich, Paul; Zulch, Peter; Harer, John

Published

2017

Language

EN