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.

Popular Abstract

Piggybacking is when a potentially malicious person sneaks behind a person with access through a secure door. We created a system which can automatically detect this with almost 100% accuracy using machine learning. It works in both real-time and for recorded data, and can make decisions on both single images and entire sequences.

While image recognition is a popular field of research, doing so for 3D point clouds, such as the output of a radar, is not as common. For us it posed unique challenges, on top of being really hard to make sense of with the human eye. There already exists camera based technology for detecting piggybacking. We shall evaluate radar as an alternative solution. It has several advantages compared to camera technology, even when not considering technical advantages such as producing three dimensional images. One can not identify a person using radar, therefore radar does not violate integrity in the same way a camera does. On top of this, producing a radar good enough for piggybacking detection is cheaper than producing a camera for the same purpose.

The essence of the thesis was to compare our radar solution to an existing camera solution developed by Axis. Additionally two different placements of the radar were compared, at the side of the door and above the door. The result was that our radar based solution performed slightly better than the camera based solution on easier scenarios, such as one or two people walking normally through a door. For these, both radar positions had accuracies over 99%. When considering more difficult scenarios, such as carrying a large box in front of you, the radars outperformed the camera significantly. When comparing the two placements of the radar, the placement above the door came out slightly ahead. From that point of view, it is easier to see whether one or two people are walking in frame.

A real world scenario where our radar based solution could be useful would be somewhere where security is not of the highest priority. It could for example be used at a subway station to detect when more than one person passes through the gates at the same time. Before deploying it at a subway station, something would have to be done about potential problems that commonly occur at the subway. Such problems could be people traveling with a suitcase or people traveling with a stroller. These are things that could be falsely detected as piggybacking, if not accounted for.

During development a lot of time was spent evaluating different neural networks to find the best possible structure for the piggybacking problem. In the end a type of recurrent neural network called LSTM performed best and was used in the final version. If you are interested in the more technical and pragmatic work of building an as good as possible model for detecting piggybacking, read chapter 3 of the full thesis.

• author: Sigurdsson, Joel and Olsson, Hannes
• supervisor: Andreas Langer
• organization: Mathematics (Faculty of Engineering)
• course: FMAM05 20222
• year: 2022
• type: H2 - Master's Degree (Two Years)
• subject: Mathematics and Statistics
• keywords: neural networks, classification, radar, access control, artificial, recurrent, convolutional, point clouds, cnn, ann, lstm, rnn, optimization, gradient descent, camera, piggybacking, tailgating, fmcw, pointnet
• publication/series: Master's Theses in Mathematical Sciences
• report number: LUTFMA-3489-2022
• ISSN: 1404-6342
• other publication id: 2022:E67
• language: English
• id: 9102952
• date added to LUP: 2022-11-09 17:04:31
• date last changed: 2022-11-09 17:04:31