Daimler Stereo Ped. Detection Benchmark Data Set
        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 Stereo Pedestrian Detection Benchmark Dataset introduced in

C. Keller, M. Enzweiler, and D. M. Gavrila, “A New Benchmark for Stereo-based Pedestrian Detection," Proc. of the IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany, 2011.

pedestrian  dataset

Pedestrian  Data Set Dataset

This new benchmark extends the previously published Daimler Mono Pedestrian Detection Benchmark with

  • 7129 stereo image pairs not containing pedestrians in the training set, from which negative samples can be extracted (positive samples remain unchanged
  • images of the second camera for the test set (a sequence with more than 21.790 images with 56.492 pedestrian labels, fully visible or partially occluded, captured from a vehicle during a 27 min drive through urban traffic, at VGA resolution (640x480, uncompressed)).
  • vehicle speed and steering angle measurements for the test set, with the yaw rate derived.

The dataset

  • specifies an evaluation setting (3D localization criterion, known ground plane, and sensor coverage area provides ROIs).
  • specifies performance metrics both at the frame- and trajectory-level (the latter also allows benchmarking of tracking algorithms).
  • provides the baseline performance of a state-of-the-art method (HOG/linSVM) on the specified training and test set.
  • is “open”: both training and test set are public and (freely) available for non-commercial purposes, see below..

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.