Siegmund, Dirk ; Fu, Biying ; Samartzidis, Timotheos ; Wainakh, Aidmar ; Kuijper, Arjan ; Braun, Andreas (2016)
Attack Detection in an Autonomous Entrance System using Optical Flow.
7th International Conference on Imaging for Crime Detection and Prevention. Madrid, Spain (23-25 Nov. 2016)
doi: 10.1049/ic.2016.0087
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Unstaffed access control portals are becoming more common in high security areas. Existing systems require expensive hardware, or are sensitive to changing environmental conditions. We present a single camera system for a mantrap which is able to verify that only one individual is in the designated transit area. Our novel approach combines optical flow and machine-learning classification. A database was created that consists of images of attempted attacks and regular verification. The results show that our approach provides competitive results and outperforms detection rates in several attack scenarios.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Siegmund, Dirk ; Fu, Biying ; Samartzidis, Timotheos ; Wainakh, Aidmar ; Kuijper, Arjan ; Braun, Andreas |
| Art des Eintrags: | Bibliographie |
| Titel: | Attack Detection in an Autonomous Entrance System using Optical Flow |
| Sprache: | Englisch |
| Publikationsjahr: | November 2016 |
| Veranstaltungstitel: | 7th International Conference on Imaging for Crime Detection and Prevention |
| Veranstaltungsort: | Madrid, Spain |
| Veranstaltungsdatum: | 23-25 Nov. 2016 |
| DOI: | 10.1049/ic.2016.0087 |
| Kurzbeschreibung (Abstract): | Unstaffed access control portals are becoming more common in high security areas. Existing systems require expensive hardware, or are sensitive to changing environmental conditions. We present a single camera system for a mantrap which is able to verify that only one individual is in the designated transit area. Our novel approach combines optical flow and machine-learning classification. A database was created that consists of images of attempted attacks and regular verification. The results show that our approach provides competitive results and outperforms detection rates in several attack scenarios. |
| Freie Schlagworte: | Guiding Theme: Digitized Work, Guiding Theme: Smart City, Research Area: Computer vision (CV), Research Area: Human computer interaction (HCI), Scene analysis, Optical flow, Computer vision, CRISP |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 06 Mai 2019 07:26 |
| Letzte Änderung: | 07 Mai 2019 08:26 |
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