Targets Classification Based on Multi-sensor Data Fusion and Supervised Learning for Surveillance Application

Jeridi, Mohamed Hechmi; Khalaifi, Hacen; Bouatay, Amine; Ezzedine, Tahar

doi:10.1007/s11277-018-6114-6

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…This factor is the density (ρ) of the sensors to be deployed, otherwise what is the necessary number of sensors to detect an intruder. In [14,31], the authors treat this idea. Indeed if we suppose that a target has a radius of influence that varies between rmin and rmax, then we can deduce the target influence area: Amin = πrmin2 and Amax = πrmax2 and by, therefore, the number (n) of sensors required to deploy in this area belongs to the interval [nmin = ρAmin, nmax = ρAmax].…”

Section: G Results and Discussionmentioning

confidence: 99%

Border Trespasser Classification Using Artificial Intelligence

et al. 2021

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Monitoring the border is a very important task for national security. Wireless sensor networks (WSN) appear well suited in this application. This work aims to monitor a large-scale geographical framework that represents the borders of countries. Researchers take the Tunisian Algerian border as an example. This border is labeled by the illegal passage of intruders between the two countries. The task is to identify the intruders and study their kinematics based on speed, acceleration, and bearing. The appropriate types of sensors are determined according to the nature of intruders. Six classification techniques are compared which are: Naïve Bayes, Support Vector Machine (SVM), Multilayer Perceptron, Best First Decision Tree (BF-Tree), Logistic Alternating Decision Tree (LAD-Tree), and J48. The comparison of the performance of the classification techniques is provided in terms of correct differentiation rates, confusion matrices, and the time taken to build each model. Four different levels of cross-validation are used to validate the classifiers. The results indicate that J48 has achieved the highest correct classification rate with a relatively low model-building time.

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Section: G Results and Discussionmentioning

confidence: 99%

Border Trespasser Classification Using Artificial Intelligence

et al. 2021

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“…On the other hand, for the agricultural sector, J.C. Zhao et al [5] proposed an IoT control application using radio frequency identification (RFID) technology RFID is a method for storing and retrieving data remotely using markers called "RFID Tag." Concerning the military domain, a research work presented in [6] [7] [8] shows an IoT application consisting of the surveillance of military zones through a WSN design. In the civil field, the article [9] [10] presented an application that consists of monitoring bridges by predicting the catastrophes that affect them.…”

Section: Introductionmentioning

confidence: 99%

IoT application for vehicles identification using the Optical Fiber Sensors and Wireless Sensor Network

Zrelli¹,

Ezzedine²

2021

Preprint

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Due to the renewed variation in government and political systems inside and outside countries, and with the high tariffs at borders, the latter have become an outlet for terrorism and smugglers. Therefore, each country seeks to develop its own protection system, and the technologies used in these systems vary according to the severity and the importance of the installations to be protected, it is found that some of them are expensive and unnecessary, but other have good and variable levels of efficiency. Consequently, the idea of designing a surveillance system that can monitor and control access becomes indispensable. In the same context, this work is of crucial strategic and geopolitical importance. It combines pre-existing alarm and monitoring methods and revolutionary Internet of Things (IoT) application products, of which Wireless Sensor Networks (WSN) and Optical Fiber Sensors (OFS) are part of this application. This article presents the distribution of wireless radar nodes accompanying with a Bragg fiber sensor to identify each rolling intruder incoming the zone to be monitored, from the determination of its speed, weight and wheelbase distance.

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A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

Wang

2020

Appl Intell

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Targets Classification Based on Multi-sensor Data Fusion and Supervised Learning for Surveillance Application

Cited by 3 publications

References 6 publications

Border Trespasser Classification Using Artificial Intelligence

Border Trespasser Classification Using Artificial Intelligence

IoT application for vehicles identification using the Optical Fiber Sensors and Wireless Sensor Network

A Dynamic multi-sensor data fusion approach based on evidence theory and WOWA operator

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