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Communication Dans Un Congrès Année : 2023

Enhanced GM-PHD filter for real time satellite multi-target tracking

Résumé

We present a real-time multi-object tracker using an enhanced version of the Gaussian mixture probability hypothesis density (GM-PHD) filter to track detections of a state-of-the-art convolutional neural network (CNN). This approach adapts the GM-PHD filter to a real-world scenario to recover target trajectories in remote sensing videos. Our GM-PHD filter uses a measurement-driven birth, considers past tracked objects, and uses CNN information to propose better hypotheses initialization. Additionally, we present a label tracking solution for the GM-PHD filter to improve identity propagation given target path uncertainties. Our results show competitive scores against other trackers while obtaining real-time performance. Code is available at https://github.com/Ayana-Inria/RFS-filters-for-satellite-videos.
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Dates et versions

hal-04029072 , version 1 (14-03-2023)

Identifiants

  • HAL Id : hal-04029072 , version 1

Citer

Camilo Aguilar, Mathias Ortner, Josiane Zerubia. Enhanced GM-PHD filter for real time satellite multi-target tracking. ICASSP 2023 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun 2023, Rhodes, Greece. ⟨hal-04029072⟩
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