Multi-view Body Part Recognition with Random Forests
published: April 3, 2014, recorded: September 2013, views: 3054
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This paper addresses the problem of human pose estimation, given images taken from multiple dynamic but calibrated cameras. We consider solving this task using a part-based model and focus on the part appearance component of such a model. We use a random forest classiﬁer to capture the variation in appearance of body parts in 2D images. The result of these 2D part detectors are then aggregated across views to produce consistent 3D hypotheses for parts. We solve correspondences across views for mirror symmetric parts by introducing a latent variable. We evaluate our part detectors qualitatively and quantitatively on a dataset gathered from a professional football game.
Download slides: bmvc2013_burenius_part_recognition_01.pdf (2.9 MB)
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