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Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles

 
: Martin, Manuel; Roitberg, Alina; Haurilet, Monica; Horne, Matthias; Reiß, Simon; Voit, Michael; Stiefelhagen, Rainer

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Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE/CVF International Conference on Computer Vision, ICCV 2019. Proceedings : 27 October - 2 November 2019, Seoul, Korea
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2019
ISBN: 978-1-7281-4803-8
ISBN: 978-1-7281-4804-5
ISBN: 978-1-7281-5023-9
S.2801-2810
International Conference on Computer Vision (ICCV) <17, 2019, Seoul>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()

Abstract
We introduce the novel domain-specific Drive&Act benchmark for fine-grained categorization of driver behavior. Our dataset features twelve hours and over 9.6 million frames of people engaged in distractive activities during both, manual and automated driving. We capture color, infrared, depth and 3D body pose information from six views and densely label the videos with a hierarchical annotation scheme, resulting in 83 categories. The key challenges of our dataset are: (1) recognition of fine-grained behavior inside the vehicle cabin; (2) multi-modal activity recognition, focusing on diverse data streams; and (3) a cross view recognition benchmark, where a model handles data from an unfamiliar domain, as sensor type and placement in the cabin can change between vehicles. Finally, we provide challenging benchmarks by adopting prominent methods for video- and body pose-based action recognition.

: http://publica.fraunhofer.de/dokumente/N-582950.html