Daimler Multi-Cue Occluded Ped. Classification
        Home Gavrila
        Daimler Ped Data
        Ped Mono Class
        Ped Mono Det
        Ped. Stereo Det
        Ped M-Cue Occl
        Ped Segmentation
        Ped Path Pred

  Recent Daimler
 publications on pedestrian detection

This page covers the Daimler Pedestrian Detection Benchmark Dataset introduced in

M. Enzweiler, A. Eigenstetter, B. Schiele and D. M. Gavrila,
Multi-Cue Pedestrian Classification with Partial Occlusion Handling,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010.

Pedestrian Data Set Dataset


Our training and test samples consist of manually labeled pedestrian and non-pedestrian bounding boxes in images captured from a vehicle-mounted calibrated stereo camera rig in an urban environment. For each manually labeled pedestrian, we created additional samples by geometric jittering. Non-pedestrian samples were the result of a shape detection pre-processing step with relaxed threshold setting, i.e. containing a bias towards more difficult patterns.

Dense stereo is computed using the semi-global matching algorithm (H. Hirschmueller, Stereo processing by semi-global matching and mutual information, IEEE PAMI, 30(2):328-341, 2008) To compute dense optical flow, we use structure- and motion-adaptive regularized flow (A. Wedel et al., Structure- and motion-adaptive regularization for high accuracy optic flow, ICCV, 2009).

Training and test samples have a resolution of 48 x 96 pixels with a 12-pixel border around the pedestrians. Note, that the experiments in our paper (see above) were done on 36 x 84 pixel images with a border of 6 pixels, i.e. crops of the provided dataset, with a three-component layout corresponding to head, torso, legs. For publication of the dataset, we chose to provide images with a larger border and without a pre-defined component layout, to allow for higher flexibility in the selection of components.

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 University of Amsterdam, as 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.

I DO NOT AGREE with the above license terms.

I AGREE with the above license terms.

[Home Gavrila] [Resume] [Research] [People] [Publications] [Datasets] [Media Coverage] [Open Positions] [Search]

Copyright © 2001-2012 Gavrila. All rights reserved. Disclaimer.