A Characterization of Ten Hidden-Surface Algorithms
ACM Computing Surveys (CSUR)
Plane-sweep algorithms for intersecting geometric figures
Communications of the ACM
3D articulated models and multiview tracking with physical forces
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Particle Filter with Analytical Inference for Human Body Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Tracking through Singularities and Discontinuities by Random Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Efficient hidden-surface removal in theory and in practice
Efficient hidden-surface removal in theory and in practice
Filtering Using a Tree-Based Estimator
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tracking of the Articulated Upper Body on Multi-View Stereo Image Sequences
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Tracking human motion using auxiliary particle filters and iterated likelihood weighting
Image and Vision Computing
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
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This article describes a multiple feature data fusion applied to a particle filter for marker-less human motion capture (HMC) by using a single camera devoted to an assistant mobile robot. Particle filters have proved to be well suited to this robotic context. Like numerous approaches, the principle relies on the projection of the model's silhouette of the tracked human limbs and appearance features located on the model surface, to validate the particles (associated configurations) which correspond to the best model-to-image fits. Our particle filter based HMC system is improved and extended in two ways. First, our estimation process is based on the so-called AUXILIARY scheme which has been surprisingly seldom exploited for tracking purpose. This scheme is shown to outperform conventional particle filters as it limits drastically the well-known burst in term of particles when considering high dimensional state-space. The second line of investigation concerns data fusion. Data fusion is considered both in the importance and measurement functions with some degree of adaptability depending on the current human posture and the environmental context encountered by the robot. Implementation and experiments on indoor sequences acquired by an assistant mobile robot highlight the relevance and versatility of our HMC system. Extensions are finally discussed.