The Driver Monitoring Dataset is the largest visual dataset for real driving actions, with footage from synchronized multiple cameras (body, face, hands) and multiple streams (RGB, Depth, IR) recorded in two scenarios (real car, driving simulator). Different annotated labels related to distraction, fatigue and gaze-head pose can be used to train Deep Learning models for Driver Monitor Systems.
This project includes a tool to annotate the dataset, inspect the annotated data and export training sets. Output annotations are formatted using VCD (Video Content Description) language.
More details of the recording and video material of DMD can be found at the official website
In addition, this repository wiki has useful information about the DMD dataset and the annotation process.
Depending the annotation problem, different annotation criteria should be defined to guarantee all the annotators produce the same output annotations.
We have defined the following criteria to be used with tool to produce consistent annotations:
- DMD Distraction-related actions annotation
Development of DMD was supported and funded by the European Commission (EC) Horizon 2020 programme (project VI-DAS, grant agreement 690772)
Developed with 💙 by:
- Paola Cañas (pncanas@vicomtech.org)
- Juan Diego Ortega (jdortega@vicomtech.org)
Contributions of ideas and comments: Marcos Nieto, Mikel Garcia, Gonzalo Pierola, Itziar Sagastiberri, Itziar Urbieta, Eneritz Etxaniz, Orti Senderos.
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