AUC Distracted Driver Dataset

22 October 2017
Yehya Abouelnaga, Hesham M. Eraqi, Mohamed N. Moustafa
The American University in Cairo


Creating a new dataset was essential to the completion of [1]’s work. The available alternatives to our dataset are: StateFarm and Southeast University (SEU) datasets. StateFarm’s dataset is to be used for their Kaggle competition purposes only (as per their regulations) [2]. As for Southeast University (SEU) dataset, it presents only four distraction postures. And, after multiple attempts to obtain it, we figured out that the authors do not make it publicly available. All the papers ([3], [4], [5], [6], [7], [8], [9]) that benchmarked against the dataset are affiliated with either Southeast University, Xi’an Jiaotong-Liverpool University, or Liverpool University, and they have at least one shared author. With that being said, the collected “distracted driver” dataset is the first publicly available for driving posture estimation research.

Camera Setup

The Distracted Driver’s dataset is collected using an ASUS ZenPhone (Model Z00UD) rear camera. The input was collected in a video format, and then, cut into individual images, each. The phone was fixed using an arm strap to the car roof handle on top of the front passenger’s seat. In our use case, this setup proved to be very flexible as we needed to collect data in different vehicles. All of the distraction activities were performed without actual driving in a parking spot.


In order to label the collected videos, we designed a simple multi-platform action annotation tool using modern web technologies: Electron, AngularJS, and Javascript. The annotation tool is open-source and publicly available at [10].


We had 31 participants from 7 different countries: Egypt (24), Germany (2), USA (1), Canada (1), Uganda (1), Palestine (1), and Morocco (1). Out of all participants, 22 were males and 9 were females. Videos were shot in 4 different cars: Proton Gen2 (26), Mitsubishi Lancer (2), Nissan Sunny (2), and KIA Carens (1). We extracted 17,308 frames distributed over the following classes: Drive Safe (3,686), Talk Passenger (2,570), Text Right (1,974), Drink (1,612), Talk Left (1,361), Text Left (1,301), Talk Right (1,223), Adjust Radio (1,220), Hair & Makeup (1,202), and Reach Behind (1,159).



In order to benchmark against our project, you need the training and testing splits:


Y. Abouelnaga, H. Eraqi, and M. Moustafa. "Real-time Distracted Driver Posture Classification". Neural Information Processing Systems (NIPS 2018), Workshop on Machine Learning for Intelligent Transportation Systems, Dec. 2018.

If you agree with the terms of the agreement contact Yehya Abouelnaga ( to download the database. Please, sign the following document and attach it along with your request: License Agreement.


  1. [1]Y. Abouelnaga, H. M. Eraqi, and M. N. Moustafa, “Real-time Distracted Driver Posture Classification,” arXiv preprint arXiv:1706.09498, 2017.
  2. [2]I. Sultan, “Academic purposes?,” Kaggle Forum. 2016 [Online]. Available at:
  3. [3]S. Yan, Y. Teng, J. S. Smith, and B. Zhang, “Driver behavior recognition based on deep convolutional neural networks,” 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), p. 636641, 2016 [Online]. Available at:
  4. [4]C. Yan, F. Coenen, and B. Zhang, “Driving posture recognition by convolutional neural networks,” IET Computer Vision, vol. 10, no. 2, pp. 103–114, 2016.
  5. [5]C. Yan, F. Coenen, and B. Zhang, “Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients,” International Journal of Vehicular Technology, vol. 2014, 2014.
  6. [6]C. H. Zhao, B. L. Zhang, X. Z. Zhang, S. Q. Zhao, and H. X. Li, “Recognition of driving postures by combined features and random subspace ensemble of multilayer perceptron classifiers,” Neural Computing and Applications, vol. 22, no. 1, pp. 175–184, 2013.
  7. [7]C. Zhao, Y. Gao, J. He, and J. Lian, “Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier,” Engineering Applications of Artificial Intelligence, vol. 25, no. 8, pp. 1677–1686, 2012.
  8. [8]C. Zhao, B. Zhang, J. Lian, J. He, T. Lin, and X. Zhang, “Classification of driving postures by support vector machines,” Proceedings - 6th International Conference on Image and Graphics, ICIG 2011, no. June 2014, pp. 926–930, 2011.
  9. [9]C. H. Zhao, B. L. Zhang, J. He, and J. Lian, “Recognition of driving postures by contourlet transform and random forests,” IET Intelligent Transport Systems, vol. 6, no. 2, pp. 161–168, 2011.
  10. [10]Y. Abouelnaga, “Action Annotation Tool.” Github, Cairo, 2017 [Online]. Available at: