Optimal fusion of video and RF data for detection and tracking with object occlusion

Benjamin Shapo; Christopher Kreucher

doi:10.1117/12.2042781

20 June 2014 Optimal fusion of video and RF data for detection and tracking with object occlusion

Benjamin Shapo, Christopher Kreucher

Author Affiliations +

Proceedings Volume 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII; 909106 (2014) https://doi.org/10.1117/12.2042781
Event: SPIE Defense + Security, 2014, Baltimore, Maryland, United States

Abstract

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.

Citation Download Citation

Benjamin Shapo, Benjamin Shapo, Christopher Kreucher, 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); doi: 10.1117/12.2042781; https://doi.org/10.1117/12.2042781

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