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Introduction

The Drive&Act dataset is a state of the art multi modal benchmark for action recognition in automated vehicles. It offers following key features:

  • 12h of video data in 29 long sequences
  • Calibrated multi view camera system with 5 views
  • Multi modal videos: NIR, Depth and Color data
  • Markerless motion capture: 3D Body Pose and Head Pose
  • Model of the static interior of the car
  • 83 manually annotated hierarchical activity labels:
    • Level 1: Long running tasks (12)
    • Level 2: Semantic actions (34)
    • Level 3: Object Interaction tripplets [action|object|location] (6|17|14)

Downloads

Copyright Fraunhofer IOSB.

Usage for research only.

If you have any questions regarding the dataset please contact: Manuel Martin and Alina Roitberg.

Location Video
Center mirror NIR
A-Column co-driver NIR Depth RGB Kinect IR
A-Column driver NIR
Ceiling NIR
Steering wheel NIR
Annotations Activities 3D Body Pose Interior

Publications

If you use the dataset please cite the following publication.

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

We introduce the novel domain-specific Drive&Act benchmark for fine-grained categorization of driver behavior. Our dataset features …
Manuel Martin*, Alina Roitberg*, Monica Haurilet, Matthias Horne, Simon Reiß, Michael Voit, Rainer Stiefelhagen, (*equal contribution, alphabetical order)
PDF Poster

Updates

Last updated on May 27, 2021 1 min read

Access restrictions removed

We removed the login requirements. Data is now available without asking for permission.

Last updated on Apr 17, 2019 1 min read

Project page setup for ICCV

Configured the webpage. Providing the data used in the ICCV publication.
Coming soon: head pose, continuous labels and a more detailed interior model

Team

Fraunhofer IOSB

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Manuel Martin

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Michael Voit

Karlsruhe Institute of Technology (KIT)

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Alina Roitberg

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Monica Haurilet

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Rainer Stiefelhagen