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Person Re-identification in a Car Seat

: Nottebaum, Moritz
: Kuijper, Arjan; Rus, Silvia

Fulltext urn:nbn:de:0011-n-5786256 (2.8 MByte PDF)
MD5 Fingerprint: ed12a641f5218b57aba1e077de7d0c8e
Created on: 6.3.2020

Darmstadt, 2020, 46 pp.
Darmstadt, TU, Bachelor Thesis, 2020
Bachelor Thesis, Electronic Publication
Fraunhofer IGD ()
automatic identification system (AIS); machine learning; capacitive proximity sensing; Lead Topic: Smart City; Research Line: Human computer interaction (HCI)

In this thesis, I enhanced a car seat with 16 capacity sensors, which collect data from the person sitting on it, which is then used to train a machine learning algorithm to re-identify the person from a group of other already trained persons. In practice, the car seat recognizes the person when he/she sits on the car seat and greets the person with their own name, enabling various customisations in the car unique to the user, like seat configurations, to be applied. Many researchers have done similar things with car seats or seats in general, though focusing on other topics like posture classification. Other interesting use cases of capacitive sensor enhanced seats involved measuring the emotions or focusing on general activity recognition. One major challenge in capacitive sensor research is the inconstancy of the received data, as they are not only affected by objects or persons near to it, but also by changing effects like humidity and temperature. My goal was to make the re-identification robust and use a learning algorithm which can quickly learn the patterns of new persons and is able to achieve satisfiable results even after getting only few training instances to learn from. Another important property was to have a learning algorithm which can operate independent and fast to be even applicable in cars. Both points were achieved by using a shallow convolutional neural network which learns an embedding and is trained with triplet loss, resulting in a computationally cheap inference. In Evaluation, results showed that neural networks are definitely not always the best choice, even though the computation time difference is insignificant. Without enough training data, they often lack in generalisation over the training data. Therefore an ensemble-learning approach with majority voting proved to be the best choice for this setup.