Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Dynamic Interaction Graphs for Driver Activity Recognition

: Martin, Manuel; Voit, Michael; Stiefelhagen, Rainer

Postprint urn:nbn:de:0011-n-6217934 (777 KByte PDF)
MD5 Fingerprint: 92e8af28d4b6412405b8f162f3c61e73
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 28.1.2021

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 : September 20 - 23, 2020, Virtual Conference
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-4150-3
ISBN: 978-1-7281-4149-7
7 pp.
International Conference on Intelligent Transportation Systems (ITSC) <23, 2020, Online>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

The drivers activities and the resulting distraction is relevant for all levels of vehicle automation. It is especially important for take-over scenarios in partially automated vehicles. To this end we investigate graph neuronal networks for pose based driver activity recognition. We focus on integrating additional input modalities like interior elements and objects and investigate how this data can be integrated in an activity recognition model. We test our approach on the Drive & Act dataset [1]. To this end we densely annotate and publish the bounding boxes of the dynamic objects contained in the dataset. Our results show that adding the additional input modalities boosts the recognition results of classes related to interior elements and objects by a large margin closing the gap to popular image based methods.