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Local null space pursuit for real-time moving object detection in aerial surveillance

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Abstract

Recently, accurate detection of moving objects has achieved via principal component pursuit (PCP). However, in the case of aerial imagery, existing PCP-based detection methods suffer from low accuracy and/or high computational loads. This paper presents a novel S-PCP method, called local null space pursuit (LNSP), which achieves a high detection accuracy and real-time performance on aerial images. LNSP models the background as a subspace that lies in a low-dimensional subspace, while the moving objects are modelled as sparse. Based on these two models, LNSP proposes a new formulation for the detection problem by using multiple local null spaces and \(\ell _1\)-norm. The performance of LNSP is evaluated on challenging aerial datasets and then compared the results with relevant current state-of-the-art methods.

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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. Local null space pursuit for real-time moving object detection in aerial surveillance. SIViP 14, 87–95 (2020). https://doi.org/10.1007/s11760-019-01528-y

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