Randomized trees for human pose detection

Conference item

This paper addresses human pose recognition from video sequences by formulating it as a classification problem. Unlike much previous work we do not make any assumptions on the availability of clean segmentation. The first step of this work consists in a novel method of aligning the training images using 3D Mocap data. Next we define classes by discretizing a 2D manifold whose two dimensions are camera viewpoint and actions. Our main contribution is a pose detection algorithm based on random forests. A bottom-up approach is followed to build a decision tree by recursively clustering and merging the classes at each level. For each node of the decision tree we build a list of potentially discriminative features using the alignment of training images; in this paper we consider Histograms of Orientated Gradient (HOG). We finally grow an ensemble of trees by randomly sampling one of the selected HOG blocks at each node. Our proposed approach gives promising results with both fixed and moving cameras.

Authors: Rogez, G; Rihan, J; Ramalingam, S; Orrite, C; Torr, PHS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2008 IEEE Conference on Computer Vision and Pattern Recognition
• Pages: 1-8
• Publication date: 2008-08-05
• Acceptance date: 2007-12-03
• Event title: 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008)
• Event location: Anchorage, Alaska
• Event start date: 2008-01-24
• Event end date: 2008-01-26
• DOI: 10.1109/cvpr.2008.4587617
• ISSN: 1063-6919
• EISBN: 9781424422432
• ISBN: 9781424422425
• Language: English
• Keywords: cameras; humans; histograms; detection algorithms; detectors; motion detection; computer vision; video sequences; decision trees; shape