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

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 comp...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-07, Vol.23 (14), p.6334
Main Authors: Pulpito, Osvaldo, Acito, Nicola, Diani, Marco, Ferri, Gabriele, Grasso, Raffaele, Zissis, Dimitris
Format: Article
Language:English
Subjects:
Algorithms
Analysis
automatic surveillance
Case studies
Computer vision
Deep learning
Fourier transforms
infrared images
Lagrange multiplier
Machine learning
Machine vision
maritime scenario
moving target detection
naval targets
Neural networks
Principal components analysis
real time
Statistical methods
Wavelet transforms
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23146334