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         author = {Duffner, Stefan and Odobez, Jean-Marc},
       projects = {Idiap},
          title = {Leveraging Colour Segmentation for Upper-Body Detection},
        journal = {Pattern Recognition},
         volume = {47},
         number = {6},
           year = {2014},
          pages = {2222-2230},
       abstract = {This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segmented using a multi-class probabilistic colour segmentation algorithm that leverages spatial as well as colour prior distributions for different semantic object regions (skin, hair, clothing, background). These multi-class soft segmentation maps are then classified as true or false upper-bodies. By further fusing the score of this latter classifier with the base detection score, the method shows a performance improvement on three different public datasets and using two different upper-body base detectors, demonstrating the complementarity of the contextual semantic colour segmentation and the base detector.},
            pdf = {https://publications.idiap.ch/attachments/papers/2014/Duffner_PR_2014.pdf}