AV-DTEC fuses audio and visual features using a state-space model and an adaptive teacher-student mechanism to estimate drone trajectories and classify drone types, achieving state-of-the-art results on the MMAUD dataset without manual annotation.
Unmanned aerial systems for civil applications: A review,
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AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and Classification
AV-DTEC fuses audio and visual features using a state-space model and an adaptive teacher-student mechanism to estimate drone trajectories and classify drone types, achieving state-of-the-art results on the MMAUD dataset without manual annotation.