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Body Pose and Context Information for Driver Secondary Task Detection

: Martin, Manuel; Popp, J.; Anneken, M.; Voit, Michael; Stiefelhagen, R.

Postprint urn:nbn:de:0011-n-5381231 (771 KByte PDF)
MD5 Fingerprint: ed3a23bd3e931d9c9d3d2e2ada9ad004
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Created on: 5.4.2019

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Intelligent Transportation Systems Society -ITSS-:
IEEE Intelligent Vehicles Symposium, IV 2018 : 26-30 June 2018, Changshu, China
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-4452-2
ISBN: 978-1-5386-4453-9
ISBN: 978-1-5386-4451-5
Intelligent Vehicles Symposium (IV) <29, 2018, Changshu/China>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Distraction of the driver by secondary tasks is already dangerous while driving manually but especially in handover situations in an automated mode this can lead to critical situations. Currently, these tasks are not taken into account in most modern cars. We present a system that detects typical distracting secondary tasks in an efficient modular way. We first determine the body pose of the driver and afterwards use recurrent neuronal networks to estimate actions based on sequences of the captured body poses. Our system uses knowledge about the surroundings of the driver that is unique to the car environment. Our evaluation shows that this approach achieves better results than other state of the art systems for action recognition on our dataset.