Daimler Mono Ped. Detection Benchmark Data Set
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 publications on pedestrian detection

This page covers the Daimler Pedestrian Detection Benchmark Dataset introduced in

M. Enzweiler and D. M. Gavrila. “Monocular Pedestrian Detection: Survey and Experiments”. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.31, no.12, pp.2179-2195, 2009.

Pedestrian Data Set Dataset

Pedestrian  Data Set Dataset

The benchmark

  • involves a large training and test set. The training set contains 15.560 pedestrian samples (image cut-outs at 48x96 resolution) and 6744 additional full images not containing pedestrians for extracting negative samples. The test set contains an independent 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). As such, the dataset is realistic and about one order of magnitude larger than other datasets at time of publication (8.5 Gb).
  • specifies two evaluation settings: one “generic” (2D bounding box overlap criterion) and one specific to pedestrian detection onboard a vehicle (3D localization criterion, known ground plane and sensor coverage area provide regions of interest, processing constraints).
  • specifies performance metrics both at the frame- and trajectory-level (the latter also allows benchmarking of tracking algorithms).
  • provides the baseline performance of three state-of-the-art methods (wavelet-based AdaBoost cascade, HOG/linSVM and a convolutional network NN/LRF) on the specified training and test set.
  • is “open”: both training and test set are made (freely) available for non-commercial purposes. .

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.

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