TY - GEN
T1 - Easy minimax estimation with random forests for human pose estimation
AU - Daphne Tsatsoulis, P.
AU - Forsyth, David
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We describe a method for human parsing that is straightforward and competes with state-of-the-art performance on standard datasets. Unlike the state-of-the-art, our method does not search for individual body parts or poselets. Instead, a regression forest is used to predict a body configuration in body-space. The output of this regression forest is then combined in a novel way. Instead of averaging the output of each tree in the forest we use minimax to calculate optimal weights for the trees. This optimal weighting improves performance on rare poses and improves the generalization of our method to different datasets. Our paper demonstrates the unique advantage of random forest representations: minimax estimation is straightforward with no significant retraining burden.
AB - We describe a method for human parsing that is straightforward and competes with state-of-the-art performance on standard datasets. Unlike the state-of-the-art, our method does not search for individual body parts or poselets. Instead, a regression forest is used to predict a body configuration in body-space. The output of this regression forest is then combined in a novel way. Instead of averaging the output of each tree in the forest we use minimax to calculate optimal weights for the trees. This optimal weighting improves performance on rare poses and improves the generalization of our method to different datasets. Our paper demonstrates the unique advantage of random forest representations: minimax estimation is straightforward with no significant retraining burden.
KW - Human pose estimation
KW - Minimax
KW - Regression
KW - Regression forests
UR - http://www.scopus.com/inward/record.url?scp=84925310886&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84925310886&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16178-5_47
DO - 10.1007/978-3-319-16178-5_47
M3 - Conference contribution
AN - SCOPUS:84925310886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 669
EP - 684
BT - Computer Vision - ECCV 2014 Workshops, Proceedings
A2 - Bronstein, Michael M.
A2 - Rother, Carsten
A2 - Agapito, Lourdes
PB - Springer
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -