Multi-view Body Part Recognition with Random Forests

In Proc. IEEE British Machine Vision Conference (BMVC 2013) [Paper]

Abstract 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.


The code for 2D discriminative part is available on github:
git clone


The data can be downloaded at the following address:
KTH Multiview Football Dataset II