Dr. Gabriele Ferri Profile

Dr. Gabriele Ferri

at STO-CMRE

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Proceedings Article | 17 October 2023 Presentation + Paper

Infrared maritime moving target detection via spatial-multiscale DMD

Osvaldo Pulpito, Giovanni Martella, Nicola Acito, Marco Diani, Giovanni Corsini, Raffaele Grasso, Gabriele Ferri, Kevin LePage

Proceedings Volume 12742, 127420B (2023) https://doi.org/10.1117/12.2680239

KEYWORDS: Target detection, Video, Detection and tracking algorithms, Object detection, Infrared radiation, Modal decomposition, Infrared detectors, Infrared sensors

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In the ambit of the computer vision, the moving object detection is an extremely important topic which has drawn the interest of the scientific community. Recently, an emerging dimensionality reduction technique, called Dynamic Mode Decomposition (DMD), has been exploited to make an estimation of the background. The DMD is a pure data-driven technique which provides information about the spatial and temporal evolution of the input video. The main idea behind the usage of the DMD is the possibility of isolating the modes that addresses the background in order to obtain the signal associated with the target by subtraction. In the practice, the DMD produces a unimodal representation of the background, which provides good results under the assumptions that the background is quasi-static, the foreground objects are small and their motion is fast. The objective of this study is to verify the applicability of the DMD in the case of InfraRed videos of maritime scenarios with extended naval targets. In this context, the foreground is neither small, nor fast. To face that problem, we propose a spatial-multiscale approach which slightly improves the detection accuracy of the DMD-based detector. The proposed approach has been tested on a real dataset collected under real operational conditions, during an experimental activity lead by the NATO STO-CMRE in February 2022 in Portovenere (Italy). The performance has been evaluated in terms of precision and recall and has been compared to other state-of-the-art moving target detection algorithms.

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