CVC-08 On-board Sequence Pedestrian Dataset 
      ------------------------------------------------------------------------------------------------

      A. Gonzalez, D. Vazquez, A. M. Lopez, J. Amores

      Advanced Driver Assistance Systems
      (C) Computer Vision Center 2014

      http://www.cvc.uab.es/adas
      agalzate@cvc.uab.es

      ------------------------------------------------------------------------------------------------

      References to this pedestrian database should be made to the following article:

@InProceedings{AGonzalez:2015,
author="Alejandro Gonzalez Alzate and Sebastian Ramos and David Vazquez and Antonio Lopez and Jaume Amores",
title="Spatiotemporal Stacked Sequential Learning for Pedestrian Detection",
booktitle="7th Iberian Conference on Pattern Recognition and Image Analysis",
year="2015",
}

      ------------------------------------------------------------------------------------------------

      Contents: 

        Dataset: 
	        	- The sequence was acquired on-board under normal urban driving conditions. The images are monochrome and of 480X960 pixels. We used a 4mm focal length lens, so providing a wide field of view. We drove during 30 minutes approximately, giving rise to a sequence of around 60,000 frames. Then, using steps of 10 frames we annotated all the pedestrians. This turns out in 7,900 annotated pedestrians, 5,400 reasonable and non occluded. We have divided the video sequence into three sequential parts, the first one for training, the last one for testing, in the middle we have leaved a gap for avoiding testing and training with the same persons. Overall we train with 3,600 reasonable pedestrians, and test on 1,300 reasonable ones.

			- NOTE: Here it is only provided the annotated frames, for having acces to the complete sequence contact authors.
	
      ------------------------------------------------------------------------------------------------
      Disclaimer: 

      The data is provided "as is" without express or implied warranty. The database is made freely 
      available to the scientific community under the Creative Commons Attribution-NonCommercial 4.0
      License, i.e., you are free to copy, distribute, transmit and adapt the work, and you must 
      attribute the work in the manner specified by the authors and not use this work for commercial 
      purposes. For more information: http://creativecommons.org/licenses/by-nc/4.0/