Robust Real-Time Face Detection
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
Multimodal Technologies for Perception of Humans
Probabilistic Head Pose Tracking Evaluation in Single and Multiple Camera Setups
Multimodal Technologies for Perception of Humans
Joint Bayesian Tracking of Head Location and Pose from Low-Resolution Video
Multimodal Technologies for Perception of Humans
Learning a Person-Independent Representation for Precise 3D Pose Estimation
Multimodal Technologies for Perception of Humans
Head Pose Estimation in Single- and Multi-view Environments - Results on the CLEAR'07 Benchmarks
Multimodal Technologies for Perception of Humans
Head Orientation Estimation Using Particle Filtering in Multiview Scenarios
Multimodal Technologies for Perception of Humans
Synchronized submanifold embedding for person-independent pose estimation and beyond
IEEE Transactions on Image Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Efficient person identification using active cameras in a smartroom
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Estimating human body and head orientation change to detect visual attention direction
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
An adaptation framework for head-pose classification in dynamic multi-view scenarios
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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We present a new system for 3D head tracking and pose estimation in low-resolution, multi-view environments. Our approach consists of a joint particle filter scheme, that combines head shape evaluation with histograms of oriented gradients and pose estimation by means of artificial neural networks. The joint evaluation resolves previous problems of automatic alignment and multi-sensor fusion and gains an automatic system that is flexible against modifications in the available number of cameras. We evaluate on the CLEAR07 dataset for multi-view head pose estimation and achieve mean pose errors of 7.2° and 9.3° for pan and tilt respectively, which improves accuracy compared to our previous work by 14.9% and 25.8%.