Learning to segment humans by stacking their body parts

E. Puertas*, M. A. Bautista, D. Sanchez, S. Escalera, O. Pujol

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)

Abstract

Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body part likelihood maps. These likelihood maps are obtained in a first stage by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline.

OriginalsprogEngelsk
TitelComputer Vision - ECCV 2014 Workshops, Proceedings
RedaktørerMichael M. Bronstein, Carsten Rother, Lourdes Agapito
Antal sider13
ForlagSpringer
Publikationsdato2015
Sider685-697
ISBN (Elektronisk)9783319161778
DOI
StatusUdgivet - 2015
Udgivet eksterntJa
Begivenhed13th European Conference on Computer Vision, ECCV 2014 - Zurich, Schweiz
Varighed: 6 sep. 201412 sep. 2014

Konference

Konference13th European Conference on Computer Vision, ECCV 2014
Land/OmrådeSchweiz
ByZurich
Periode06/09/201412/09/2014
SponsorAdobe, Amazon, Ascending Technologies, Bosch, Disney Research, Facebook, Google, IJAC, iniLabs, Leica, M-tec, Microsoft Research, Mitsubishi Electric, mOBILEYE, NICTA, NVIDIA, OMRON Corporation, Pelican imaging, Qualcomm, Siemens, SUPERFISH, technicolor, University of Adelaide, The University of North Carolina, Toshiba Corporation, Toyota, VIZrt
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind8925
ISSN0302-9743

Bibliografisk note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

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