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MARO: matrix rank optimization for the detection of small-size moving objects from aerial camera platforms

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Abstract

Moving objects detection from aerial camera platforms is a very challenging problem due to the small-size of the moving objects and the false motion of the static background elements. Although many methods have been proposed in this domain, they always have a trade-off between true detections and false detections. This paper proposes a novel solution called matrix rank optimization method (MARO) to achieve high true detections with low false detections. In MARO, the detection problem is formulated as a principal component pursuit with a transformation domain. The novelty of MARO is that it solves this problem by using the inexact Newton method and a backtracking behaviour in inexact augmented Lagrange multiplier. MARO has been extensively evaluated using DARPA VIVID, UCF aerial action, and VIRAT aerial datasets. The results show that MARO outperforms current-state-of-the-art methods, as well as lowers the execution time without sacrificing the accuracy.

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Authors and Affiliations

  1. Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada

    Agwad ElTantawy & Mohamed S. Shehata

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  1. Agwad ElTantawy
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  2. Mohamed S. Shehata
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Correspondence to Agwad ElTantawy.

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ElTantawy, A., Shehata, M.S. MARO: matrix rank optimization for the detection of small-size moving objects from aerial camera platforms. SIViP 12, 641–649 (2018). https://doi.org/10.1007/s11760-017-1203-7

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  • DOI: https://doi.org/10.1007/s11760-017-1203-7

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