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Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13806))

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Abstract

This paper concerns a methodology of a semi-automatic annotation strategy for the gaze estimation material of the Driver Monitoring Dataset (DMD). It consists of a pipeline of semi-automatic annotation that uses ideas from Active Learning to annotate data with an accuracy as high as possible using less human intervention. A dummy model (the initial model) that is improved by iterative training and other state-of-the-art (SoA) models are the actors of an automatic label assessment strategy that will annotate new material. The newly annotated data will be used as an iterative process to train the dummy model and repeat the loop. The results show a reduction of annotation work for the human by 60%, where the automatically annotated images have a reliability of 99%.

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Notes

  1. 1.

    https://dmd.vicomtech.org/.

  2. 2.

    https://github.com/Vicomtech/DMD-Driver-Monitoring-Dataset/tree/master/annotation-tool.

  3. 3.

    https://github.com/Vicomtech/DMD-Driver-Monitoring-Dataset/tree/master/exploreMaterial-tool.

  4. 4.

    https://vcd.vicomtech.org/.

  5. 5.

    https://github.com/erkil1452/gaze360.

  6. 6.

    https://github.com/hysts/pytorch_mpiigaze_demo.

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Acknowledgement

This work has received funding from the Basque Government under project AutoEv@l of the program ELKARTEK 2021.

Author information

Authors and Affiliations

  1. Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastián, Spain

    Teun Urselmann, Paola Natalia Cañas, Juan Diego Ortega & Marcos Nieto

  2. Eindhoven University of Technology (TU/e), 5600 MB, Eindhoven, The Netherlands

    Teun Urselmann

  3. University of the Basque Country (UPV/EHU), Campus Donostia, 20018, Spain

    Paola Natalia Cañas

Authors
  1. Teun Urselmann
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  2. Paola Natalia Cañas
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  3. Juan Diego Ortega
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  4. Marcos Nieto
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Corresponding author

Correspondence to Paola Natalia Cañas .

Editor information

Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Urselmann, T., Cañas, P.N., Ortega, J.D., Nieto, M. (2023). Semi-automatic Pipeline for Large-Scale Dataset Annotation Task: A DMD Application. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13806. Springer, Cham. https://doi.org/10.1007/978-3-031-25075-0_38

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  • DOI: https://doi.org/10.1007/978-3-031-25075-0_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25074-3

  • Online ISBN: 978-3-031-25075-0

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