LostAndFoundDataset

The LostAndFound Dataset addresses the problem of detecting unexpected small obstacles on the road often caused by lost cargo.
The dataset comprises 112 stereo video sequences with 2104 annotated frames (picking roughly every tenth frame from the recorded data).

If you are using this dataset in a publication please cite the following paper:
Peter Pinggera, Sebastian Ramos, Stefan Gehrig, Uwe Franke, Carsten Rother, Rudolf Mester, "Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles", Proceedings of IROS 2016, Daejeon, Korea. Link to the paper

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For the data format and the interpretation of the data sources we refer to the description of the Cityscapes dataset format which we closely follow: http://www.cityscapes-dataset.com

Below you can find a link to the data description and some development kit (tailored for Cityscapes but applicable to LostAndFound as well):
https://github.com/mcordts/cityscapesScripts
In order to replace the cityscapes mapping with lostAndFound labels replace labels.py in the development kit with this file: labels.py
A description of the labels of the LostAndFound dataset can be found here: laf_table.pdf

In the near future we will publish results on the dataset for several metrics. Stay tuned for more. If you want to appear on the list write an e-mail to Sebastian Ramos (sebastian.ramos@daimler.com).

Below, you can find all currently available downloads. A README and various scripts for inspection, preparation, and evaluation can be found in above git repository.
The following packages are available for download:
      


    gtCoarse.zip (37MB) annotations for train and test sets
    (2104 annotated images)
  
     
    leftImg8bit.zip
  (6GB) left 8-bit images - train and test set
    (2104 images)
    rightImg8bit.zip  (6GB) right 8-bit images - train and test set
    (2104 images)
                      
   
 
    leftImg16bit.zipl (17GB) right 16-bit images - train and test set
    (2104 images)
    rightImg16bit.zip (17GB) right 16-bit images - train and test set
    (2104 images)
  
 
    disparity.zip (1.4GB) depth maps using Semi-Global Matching for
    train and test set (2104 images)
             
 
    timestamp.tgz (50kB) timestamps for train and test sets
    camera.zip  (1MB) Intrinsic and extrinsic camera parameters for
    train and test sets
    vehicle.zip  (1MB) vehicle odometry data (speed and yaw rate)
    for train and test sets

  
The LostAndFound dataset may be used according to the following license agreement: license.txt

For questions, suggestions, and comments contact Sebastian Ramos (sebastian.ramos@daimler.com) or Stefan Gehrig.

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