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Occlusions can degrade object tracking performance in sensor imaging systems. This paper describes a robust approach
to object tracking that fuses video frames with RF data in a Bayes-optimal way to overcome occlusion. We fuse data
from these heterogeneous sensors, and show how our approach enables tracking when each modality cannot track
individually. We provide the mathematical framework for our approach, details about sensor operation, and a
description of a multisensor detection and tracking experiment that fuses real collected image data with radar data.
Finally, we illustrate two benefits of fusion: improved track hold during occlusion and diminished error.
Benjamin Shapo andChristopher Kreucher
"Optimal fusion of video and RF data for detection and tracking with object occlusion", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 909106 (20 June 2014); https://doi.org/10.1117/12.2042781
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Benjamin Shapo, Christopher Kreucher, "Optimal fusion of video and RF data for detection and tracking with object occlusion," Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 909106 (20 June 2014); https://doi.org/10.1117/12.2042781