Daimler Pedestrian Path Prediction GCPR'13
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  Recent Daimler
 publications on pedestrian detection

This page covers the Daimler Pedestrian Path Prediction Benchmark Dataset introduced in

N. Schneider and D. M. Gavrila.
Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study.
In Lecture Notes in Computer Science: Proc. of the German Conference on Pattern Recognition (GCPR), vol. 8142, Springer, 2013.

Bending In

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The dataset contains a collection of 68 pedestrian sequences collected from a stationary and moving vehicle. Four different pedestrian motion types are considered: crossing, stopping, starting to walk and bending-in.There is not more than one pedestrian in the sequences; pedestrians are not occluded.

We provide

  • original stereo pairs (8 bit PGM, 1176x640)
  • calibration data
  • ground truth (GT) annotations,
  • pedestrian detector measurements and
  • vehicle data (speed, yaw-rate)
  • event tags and time-to-event labels (TTE in frames).

In particular, we provide trajetory data (GT annotations, detector measurements) in (u, d) form, where u is the lateral image coordinate and d is disparity. With the static ground plane assumption this allows the computation of the pedestrian location on the ground plane (X, Z), also provided.

Pedestrian bounding boxes (i.e. measured lateral image location) were computed with a HOG/linSVM-based detector. Disparity d was computed by means of (H. Hirschmueller, Stereo processing by semi-global matching and mutual information, IEEE PAMI, 30(2):328-341, 2008). The availability of the stereo images allows the experimentation with a different pedestrian detector and/or stereo algorithm. 

The sequences have a total of 19612 stereo image pairs. 12485 images contain manually labeled pedestrian bounding boxes and 9366 images containing pedestrian detector measurements. In our paper, we only evaluate in a distance range of 5-50 and require “valid” disparity measurements. This selection leads to 9152 ground truth and 7937 measurement objects.

For more information, see our above GCPR’13 publication or read the documentation.

License Terms

This dataset is made freely available to academic and non-academic entities for  non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use, copy, and distribute the data given that you agree:

  1. That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, Daimler (or the website host) does not accept any responsibility for errors or omissions.
  2. That you include a reference to the above publication in any published work that makes use of the dataset.
  3. That if you have altered the content of the dataset or created derivative work, prominent notices are made so that any recipients know that they are not receiving the original data.
  4. That you may not use or distribute the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
  5. That this original license notice is retained with all copies or derivatives of the dataset.
  6. That all rights not expressly granted to you are reserved by Daimler.

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