Abstract
Data sets are a fundamental tool for comparing detection algorithms, fostering advances in the state of the art. The INRIA person data set is very popular in the Pedestrian Detection community, both for training detectors and reporting results. Yet, the labelling of its test set has some limitations: some of the pedestrians are not labelled, there is no specific label for the ambiguous cases and the information on the visibility ratio of each person is missing. We present a new labelling that overcomes such limitations and show that it can be used to evaluate the performance of detection algorithms in a more truthful way.
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Taiana, M., Nascimento, J.C., Bernardino, A. (2013). An Improved Labelling for the INRIA Person Data Set for Pedestrian Detection. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_34
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DOI: https://doi.org/10.1007/978-3-642-38628-2_34
Publisher Name: Springer, Berlin, Heidelberg
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