Daimler Mono Ped. Classification Benchmark Data Set
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This page covers the Daimler Pedestrian Classification Benchmark Dataset introduced in

S. Munder and D. M. Gavrila. “An Experimental Study on Pedestrian Classification”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp.1863-1868, November 2006.

Pedestrian Dataset

The dataset contains a collection of pedestrian and non-pedestrian images. It is made available for download on this site for benchmarking purposes, in order to advance research on pedestrian classification. 

Pedestrian Data Set Dataset

The dataset consists of two parts:

  • a base data set. The base data set contains a total of 4000 pedestrian- and 5000 non-pedestrian samples cut out from video images and scaled to common size of 18x36 pixels. This data set has been used in Section VII-A of the paper referenced above. 

    Pedestrian images were obtained from manually labeling and extracting the rectangular positions of pedestrians in video images.  Video images were recorded at various (day) times and locations with no particular constraints on pedestrian pose or clothing, except that pedestrians are standing in upright position and are fully visible. As non-pedestrian images, patterns representative for typical preprocessing steps within a pedestrian classification application, from video images known not to contain any pedestrians. We chose to use a shape-based pedestrian detector that matches a given set of pedestrian shape templates to distance transformed edge images (i.e. comparatively relaxed matching threshold).
  • additional non-pedestrian images. An additional collection of 1200 video images NOT containing any pedestrians, intended for the extraction of additional negative training examples. Section V of the paper referenced above describes two methods on how to increase the training sample size from these images, and Section VII-B lists experimental results.

For more details on the benchmark dataset, see the associated README file.

The Daimler Pedestrian Classification Benchmark was referenced more than 50 times (source: Web of Science). It was actually used in the following publications:

  • F. De la Torre and O. Vinyals. “Learning Kernel Expansions for Image Classification”. CVPR 2007.
  • P. Dollar, Z. Thu, H. Tao and S. Belongie. “Feature Mining for Image Classification”. CVPR 2007.
  • L. Nanni and A. Lumini. “Ensemble of Multiple Pedestrian Representations”. IEEE Trans. on Intelligent Transportation Systems. Vol.9, nr.2, pp. 365-369. 2008
  • S. Maji, A. Berg and J. Malik. “Classification using Intersection Kernel Support Vector Machines is Efficient”. CVPR 2008.
  • O. Tuzel, F. Porikli and P. Meer. “Pedestrian Detection via Classification on Riemannian Manifolds.” IEEE Trans on Pattern Analysis Machine Intelligence, vol. 30, no 10, pp.1713-1727, October 2008.
  • D. Anguita, S. Pischiutta and S. Ridella. “A support vector machine with integer parameters”. Neurocomputing, vol. 72, nrs.1-3, 2008.
  • S. Maji and A. Berg. “Max-Margin Additive Classifiers for Detection”. ICCV’2009.
  • H. Jung and J. Kim. “Constructing a pedestrian recognition system with a public open database, without the necessity of re-training: an experimental study”. Pattern Analysis Applications. DOI 10.1007/s10044-009-0153-2.
  • L. Oliveira, U. Nunes and P. Peixoto. “On Exploration of Classifier Ensemble Synergism in Pedestrian Detection”. IEEE Trans. on Intelligent Transportation Systems, vol.11, nr.1, pp.16-27, 2010.

Contact me if you want you paper added here. Please note that only an identical test methodology as in the original paper (e.g. training-test split) allows true comparisons.

License Terms

This dataset is made available to the scientific community for non-commercial research 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, DaimlerChrysler (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 DaimlerChrysler.

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