Daimler Pedestrian Segmentation Benchmark
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        Daimler Ped Data
        Ped Mono Class
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        Ped. Stereo Det
        Ped M-Cue Occl
        Ped Segmentation
        Ped Path Pred

  Recent Daimler
 publications on pedestrian detection

This page covers the Daimler Pedestrian Segmentation Benchmark Dataset introduced in

F. Flohr and D. M. Gavrila.
PedCut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues. Proc. of the British Machine Vision Conference, Bristol, UK, 2013




Our dataset consist of manually contour-labeled pedestrian images captured from a vehicle-mounted calibrated stereo camera rig in an urban environment. For each pedestrian cutout we provide a 24 bit PNG image, a float disparity map and a ground truth shape.

Dense stereo is computed using the semi-global matching algorithm (H. Hirschmueller, Stereo processing by semi-global matching and mutual information, IEEE Trans. on PAMI, 30(2):328-341, 2008).

The 785 image cut-outs have a height between 34 and 468 pixels and a width between 11 and 267 pixels. In our BMVC’13 publication only samples with a height greater than 120 pixels are used. We provide the samples with an additional 10 % border to each side.

The dataset that we used for training our Boosted Decision Tree Ensemble derives from the publication
T. Scharwchter, M. Enzweiler, U. Franke, and S. Roth. “Efficient Multi-Cue Scene Segmentation”. In Lecture Notes in Computer Science (Proc. of the German Conference on Pattern Recognition (GCPR)), volume 8142. Springer, 2013. It can be downloaded here.

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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