Attack detection in an autonomous entrance system using optical flow

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.