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An end-to-end neural network for detecting hidden people in images based on multiple attention network

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Abstract

Camouflaged people like soldiers on the battlefield or even camouflaged objects in the natural environments are hard to be detected because of the strong resemblances between the hidden target and the background. That’s why seeing these hidden objects is a challenging task. Due to the nature of hidden objects, identifying them require a significant level of visual perception. To overcome this problem, we present a new end-to-end framework via a multi-level attention network in this paper. We design a novel inception module to extract multi-scale receptive fields features aiming at enhancing feature representation. Furthermore, we use a dense feature pyramid taking advantage of multi-scale semantic features. At last, to locate and distinguish the camouflaged target better from the background, we develop a multi-attention module that generates more discriminative feature representation and combines semantic information with spatial information from different levels. Experiments on the camouflaged people dataset show that our approach outperformed all state-of-the-art methods.

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Funding

This research received no external funding.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon, Turkey

    Rabeb Hendaoui & Vasif Nabiyev

Authors
  1. Rabeb Hendaoui
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  2. Vasif Nabiyev
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Contributions

Conceptualization, and methodology, V.N.; software, R.H; formal analysis, V.N and R.H.; resources, R.H; data curation, R.H.; writing—original draft preparation, R.H..; writing—review and editing, R.H. and V.N.; visualization, V.N and R.H..; supervision, V.N.

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Correspondence to Rabeb Hendaoui.

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Hendaoui, R., Nabiyev, V. An end-to-end neural network for detecting hidden people in images based on multiple attention network. Multimed Tools Appl 81, 18531–18542 (2022). https://doi.org/10.1007/s11042-022-12118-5

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  • DOI: https://doi.org/10.1007/s11042-022-12118-5

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