%Aigaion2 BibTeX export from Idiap Publications
%Friday 22 November 2024 03:38:29 PM

@INPROCEEDINGS{Berclaz_VISAPP_2008,
         author = {Berclaz, Jerome and Fleuret, Francois and Fua, Pascal},
       projects = {Idiap, IM2},
          title = {Principled Detection-by-classification from Multiple Views},
      booktitle = {proceedings of the International Conference on Computer Vision Theory and Applications},
         volume = {2},
           year = {2008},
       abstract = {Machine-learning based classification techniques have been shown to be effective at detecting objects in com-
plex scenes. However, the final results are often obtained from the alarms produced by the classifiers through a
post-processing which typically relies on ad hoc heuristics. Spatially close alarms are assumed to be triggered
by the same target and grouped together.

Here we replace those heuristics by a principled Bayesian approach, which uses knowledge about both the
classifier response model and the scene geometry to combine multiple classification answers. We demonstrate
its effectiveness for multi-view pedestrian detection.

We estimate the marginal probabilities of presence of people at any location in a scene, given the responses
of classifiers evaluated in each view. Our approach naturally takes into account both the occlusions and the
very low metric accuracy of the classifiers due to their invariance to translation and scale. Results show our
method produces one order of magnitude fewer false positives than a method that is representative of typical
state-of-the-art approaches. Moreover, the framework we propose is generic and could be applied to any
detection-by-classification task.}
}