Multi-view body part recognition with random forests
2013 (English)In: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, Bristol, England: British Machine Vision Association , 2013Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Bristol, England: British Machine Vision Association , 2013.
Data processing, Decision trees, Motion estimation, Body part recognition, Calibrated cameras, Football game, Human pose estimations, Latent variable, Part-based models, Random forest classifier, Random forests
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:kth:diva-134190DOI: 10.5244/C.27.48ISI: 000346352700045ScopusID: 2-s2.0-84898413079OAI: oai:DiVA.org:kth-134190DiVA: diva2:665190
2013 24th British Machine Vision Conference, BMVC 2013; Bristol; United Kingdom; 9 September 2013 through 13 September 2013
FunderEU, FP7, Seventh Framework Programme
QC 201312172013-11-192013-11-192015-10-06Bibliographically approved