Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
In this paper an approach to recover the 3D human body pose from static images is proposed. We adopt a discriminative learning technique to directly infer the 3D pose from appearance-based local image features. We use simplified Gradient Location and Orientation histogram (GLOH) as our image feature representation. We then employ the gradient tree-boost regression to train a discriminative model for mapping from the feature space to the 3D pose space. The training and evaluation of our algorithm were conducted on the walking sequences of a synchronized video and 3D motion dataset. We show that appearance-based local features can be used for pose estimation even in cluttered environments. At the same time, the discriminatively learned model allows the 3D pose to be estimated in real time.