Identifying Piggybacking with Radar and Neural Networks

Abstract: A common problem in access control is piggybacking. This is when a person without authorized access sneaks closely behind another with access through a door. This thesis seeks to answer whether using radar is a viable solution when attempting to detect piggybacking. Detection will be made by classifying sequences of point clouds generated by the radar, using neural networks. The thesis compares two different placements of the radar, at the side of- and above a door, with an existing camera based piggybacking detection solution. In addition to comparing the results, the development of the model will be described in detail. This includes exploring different architectures for the neural network(s). Moreover, strengths and weaknesses of radar technology, compared to camera technology will be discussed. The results show that all three solutions perform well, with accuracy above 99\% when one or two people are walking normally in frame. When comparing the solutions on more challenging scenarios such as one person carrying a big box or two people hugging while walking, both radar based solutions outperform the camera based solution. In general, slightly better separation between people can be seen in the point clouds generated by the radar placed above the door. This resulted in slightly better performance compared to the placement at the side of the door in certain scenarios. In a world where privacy and integrity is more valued than ever, radar has a big role to play in modern access control solutions. The results from this thesis show that a radar can perform at the same level, and sometimes better than a camera for detecting piggybacking.