A Hybrid Robotic Network for Maritime Situational Awareness: Results From the INFORE22 Sea Trial

Ferri, Gabriele; Grasso, Raffaele; Faggiani, Alessandro; Rosa, Francesca de; Camossi, Elena; Grati, Alberto; Stinco, Pietro; Teseï, Alessandra; Been, Robert; LePage, Kevin D.; Bereta, Konstantina; Vodas, Marios; Zissis, Dimitris

doi:10.1109/oceans47191.2022.9977142

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The choice was essentially driven by the noticeable speed of the mentioned techniques, which did not significantly increase the overall computational load. The last contribution of this paper is the evaluation of the proposed technique compared with a broadly used technique based on GMM [ 18 ], on a vast dataset of operationally significant video sequences, collected by the NATO Science and Technology Organization, Centre for Maritime Research and Experimentation (NATO STO-CMRE) during the Interactive Extreme-Scale Analytics and Forecasting (INFORE) project campaign led in Portovenere (La Spezia, Italy) during February 2022 [ 42 ]. The area of operation is shown in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios

Pulpito

Acito

Diani

et al. 2023

Sensors

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Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called “saliency-aided online moving window RPCA” (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach.

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Section: Introductionmentioning

confidence: 99%

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios

Pulpito

Acito

Diani

et al. 2023

Sensors

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“…The validity of such considerations and performance statements is supported by the vastness and significance of the dataset upon which the algorithm was tested. Such dataset of operationally significant video sequences, which is provided by the NATO Science and Technology Organization, Centre for Maritime Research and Experimentation (NATO STO-CMRE), was collected during the Interactive Extreme-Scale Analytics and Forecasting (INFORE) project campaign carried out in Portovenere (La Spezia, Italy) in February 2022 [15] and depicts several targets characterized by different size and speed. The area of operation of the INFORE project is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Infrared maritime moving target detection via spatial-multiscale DMD

Pulpito,

Martella,

Acito

et al. 2023

Artificial Intelligence for Security and Defence Applications

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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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A Robotic Cooperative Network for Localising a Submarine in Distress: Results From REPMUS21

Ferri

Faggiani

Petroccia

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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A Hybrid Robotic Network for Maritime Situational Awareness: Results From the INFORE22 Sea Trial

Cited by 3 publications

References 19 publications

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios

Infrared maritime moving target detection via spatial-multiscale DMD

A Robotic Cooperative Network for Localising a Submarine in Distress: Results From REPMUS21

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