@inproceedings{caputo2009clef,
  author =       {Caputo, Barbara and Pronobis, Andrzej and Jensfelt, Patric},
  title =        {Overview of the {CLEF} 2009 {R}obot {V}ision Track},
  booktitle =    {Working Notes for the {CLEF} 2009 Workshop},
  year =         2009,
  isbn =         {978-88-88506-84-5},
  issn =         {1818-8044},
  address =      {Corfu, Greece},
  url =          {http://www.pronobis.pro/publications/caputo2009clef},
  abstract =     {The robot vision task has been proposed to the ImageCLEF participants for the first time in 2009. The task attracted a considerable attention, with 19 inscribed research groups, 7 groups eventually participating and a total of 27 submitted runs. The task addressed the problem of visual place recognition applied to robot topological localization. Specifically, participants were asked to classify rooms on the basis of image sequences, captured by a perspective camera mounted on a mobile robot. The sequences were acquired in an office environment, under varying illumination conditions and across a time span of almost two years. The training and validation set consisted of a subset of the IDOL2 database. The test set consisted of sequences similar to those in the training and validation set, but acquired 20 months later and imaging also additional rooms. Participants were asked to build a system able to answer the question “where are you?” (I am in the kitchen, in the corridor, etc) when presented with a test sequence imaging rooms seen during training, or additional rooms that were not imaged in the training sequence. The system had to assign each test image to one of the rooms present in the training sequence, or indicate that the image came from a new room. We asked all participants to solve the problem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). Of the 27 runs, 21 were submitted to the obligatory task, and 6 to the optional task. The best result in the obligatory task was obtained by the Multimedia Information Retrieval Group of the University of Glasgow, UK with an approach based on local feature matching. The best result in the optional task was obtained by the Intelligent Systems and Data Mining Group (SIMD) of the University of Castilla-La Mancha, Albacete, Spain, with an approach based on local features and a particle filter.}
}