Abstract
In surveillance application scenarios, like border security and area monitoring, potential targets to be detected may be either an unarmed person, a soldier carrying ferrous weapon or a vehicle. Detection is the first phase of a monitoring process, followed by the target classification phase and finally their tracking if required. This work focuses on classification step, where we introduce our classification approach not too resource-intensive, easy to implement and suitable for large scale environment. For that, we used probabilistic reasoning techniques to address multi sensing data correlation and take advantage of multi-sensor data fusion, then, based on adopted fusion architecture, we implemented our trained classification model in a fusion node, to make the classification more accurate.
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29 January 2024
The original version of this article was revised: In this article the author’s name Khlaifi was incorrectly written as Khalaifi. The original article has been corrected.
07 February 2024
A Correction to this paper has been published: https://doi.org/10.1007/s11277-024-10857-2
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Acknowledgment
This work was supported by Communication System Laboratory “Sys’Com” in National Engineering School of Tunis, University Tunis El Manar Tunisia; we would like to thank the administrative and responsible team.
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The original version of this article was revised: In this article the author’s name Khlaifi was incorrectly written as Khalaifi. The original article has been corrected.
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Jeridi, M.H., Khlaifi, H., Bouatay, A. et al. Targets Classification Based on Multi-sensor Data Fusion and Supervised Learning for Surveillance Application. Wireless Pers Commun 105, 313–333 (2019). https://doi.org/10.1007/s11277-018-6114-6
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DOI: https://doi.org/10.1007/s11277-018-6114-6