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Multi-view body part recognition with random forests
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-4181-2753
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0001-5211-6388
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2013 (English)In: BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013, Bristol, England: British Machine Vision Association , 2013Conference paper, Published paper (Refereed)
Abstract [en]

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.
Keyword [en]
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
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-134190DOI: 10.5244/C.27.48ISI: 000346352700045Scopus ID: 2-s2.0-84898413079OAI: oai:DiVA.org:kth-134190DiVA: diva2:665190
Conference
2013 24th British Machine Vision Conference, BMVC 2013; Bristol; United Kingdom; 9 September 2013 through 13 September 2013
Funder
EU, FP7, Seventh Framework Programme
Note

QC 20131217

Available from: 2013-11-19 Created: 2013-11-19 Last updated: 2015-10-06Bibliographically approved
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