In this article, we present a smart gravimetric system for the automatic security monitoring of the accesses to public places with some entrance ticket or pass required (e.g., railway stations, subway stations, museums, exhibitions). The main objective is to spot illegal behaviors, for example, two people trying to enter using only one ticket; simultaneously, other events of interest can also be detected, for example, the passage of a person with disabilities or a person with a stroller. These pieces of information can be used to arrange the appropriate assistance or to conduct statistical analyses on the monitored place frequentation. The proposed system is based on a robust gravimetric footboard, composed of steel plates laid on load cells. The data processing, classification included, is totally executed on embedded STM32 microcontrollers, following the edge-computing paradigm. The classification exploits a MobileNetV2, a class of neural networks for computer vision, especially created for embedded and mobile applications. We realized a prototype of the gravimetric system for laboratory testing and we acquired a set of samples for the considered event classes to assess the MobileNet network capabilities. We compared the performance of this implementation with a more classic VGG16 convolutional neural network (CNN) running on a PC machine. We show that in the face of akin classification results and similar execution times between the two, the embedded MobileNet version brings many advantages, namely portability, cost, size, energy consumption reduction, and finally simplification in the installation procedure.

Addabbo, T., Fort, A., Mugnaini, M., Vignoli, V., Intravaia, M., Tani, M., et al. (2022). Smart Gravimetric System for Enhanced Security of Accesses to Public Places Embedding a MobileNet Neural Network Classifier. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 71 [10.1109/TIM.2022.3162270].

Smart Gravimetric System for Enhanced Security of Accesses to Public Places Embedding a MobileNet Neural Network Classifier

Tommaso Addabbo;Ada Fort;Marco Mugnaini;Valerio Vignoli;Matteo Intravaia;Monica Bianchini;Franco Scarselli;Barbara Toniella Corradini
2022

Abstract

In this article, we present a smart gravimetric system for the automatic security monitoring of the accesses to public places with some entrance ticket or pass required (e.g., railway stations, subway stations, museums, exhibitions). The main objective is to spot illegal behaviors, for example, two people trying to enter using only one ticket; simultaneously, other events of interest can also be detected, for example, the passage of a person with disabilities or a person with a stroller. These pieces of information can be used to arrange the appropriate assistance or to conduct statistical analyses on the monitored place frequentation. The proposed system is based on a robust gravimetric footboard, composed of steel plates laid on load cells. The data processing, classification included, is totally executed on embedded STM32 microcontrollers, following the edge-computing paradigm. The classification exploits a MobileNetV2, a class of neural networks for computer vision, especially created for embedded and mobile applications. We realized a prototype of the gravimetric system for laboratory testing and we acquired a set of samples for the considered event classes to assess the MobileNet network capabilities. We compared the performance of this implementation with a more classic VGG16 convolutional neural network (CNN) running on a PC machine. We show that in the face of akin classification results and similar execution times between the two, the embedded MobileNet version brings many advantages, namely portability, cost, size, energy consumption reduction, and finally simplification in the installation procedure.
Addabbo, T., Fort, A., Mugnaini, M., Vignoli, V., Intravaia, M., Tani, M., et al. (2022). Smart Gravimetric System for Enhanced Security of Accesses to Public Places Embedding a MobileNet Neural Network Classifier. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 71 [10.1109/TIM.2022.3162270].
File in questo prodotto:
File Dimensione Formato  
Smart_Gravimetric_System_for_Enhanced_Security_of_Accesses_to_Public_Places_Embedding_a_MobileNet_Neural_Network_Classifier.pdf

non disponibili

Tipologia: PDF editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 3.23 MB
Formato Adobe PDF
3.23 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/1201343