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A look at feet: Recognizing tailgating via capacitive sensing

: Siegmund, Dirk; Dev, Sudeep; Fu, Biying; Scheller, Doreen; Braun, Andreas


Streitz, Norbert (Ed.):
Distributed, ambient and pervasive interactions. 5th International Conference, DAPI 2018. Pt.2: Technologies and contexts : Held as part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018; Proceedings
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 10922)
ISBN: 978-3-319-91130-4 (Print)
ISBN: 978-3-319-91131-1 (Online)
International Conference on Distributed, Ambient, and Pervasive Interactions (DAPI) <6, 2018, Las Vegas/Nev.>
International Conference on Human-Computer Interaction (HCI International) <20, 2018, Las Vegas/Nev.>
Fraunhofer IGD ()
Guiding Theme: Smart City; Research Area: Computer vision (CV); Research Area: Human computer interaction (HCI); computer vision; people detection; access control; capacitive proximity sensing

At many every day places, the ability to be reliably able to determine how many individuals are within an automated access control area, is of great importance. Especially in high-security areas such as banks and at country borders, access systems like mantraps or drop-arm turnstiles serve this purpose. These automated systems are designed to ensure that only one person can pass through a particular transit area at a time. State of the art systems use camera systems mounted in the ceiling to detect people sneaking in behind authorized individuals to pass through the transit space (tailgating attacks). Our novel method is inspired by recently achieved results in capacitive in-door-localization. Instead of estimating the position of humans, the pervasive capacitance of feet in the transit space is measured to detect tailgating attacks. We explore suitable sensing techniques and sensor-grid layout to be used for that application. In contrast to existing work, we use machine learning techniques for classification of the sensor’s feature vector. The performance is evaluated on hardware-level, by defining its physical effectiveness. Tests with simulated attacks show its performance in comparison with competitive camera-image methods. Our method provides verification of tailgating attacks with an equal-error-rate of 3.5%, which outperforms other methods. We conclude with an evaluation of the amount of data needed for classification and highlight the usefulness of this method when combined with other imaging techniques.