A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Using space to describe space: Perspective inspeech, sign, and gesture
Spatial Cognition and Computation
Real-Time Hand-Arm Motion Analysis using a single Video Camera
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selecting Discriminative Tracking Features Using Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Visual Hand Tracking Algorithms
GMAI '06 Proceedings of the conference on Geometric Modeling and Imaging: New Trends
Robust Tracking for Processing of Videos of Communication's Gestures
Gesture-Based Human-Computer Interaction and Simulation
Object tracking with particle filter using color information
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Effective appearance model and similarity measure for particle filtering and visual tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Segmentation of the face and hands in sign language video sequences using color and motion cues
IEEE Transactions on Circuits and Systems for Video Technology
GW'11 Proceedings of the 9th international conference on Gesture and Sign Language in Human-Computer Interaction and Embodied Communication
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This article deals with the posture reconstruction from a mono view video of a signed utterance. Our method makes no use of additional sensors or visual markers. The head and the two hands are tracked by means of a particle filter. The elbows are detected as convolution local maxima. A non linear filter is first used to remove the outliers, then some criteria using French Sign Language phonology are used to process the hand disambiguation. The posture reconstruction is achieved by using inverse kinematics, using a Kalman smoothing and the correlation between strong and week hand depth that can be noticed in the signed utterances. The article ends with a quantitative and qualitative evaluation of the reconstruction. We show how the results could be used in the framework of automatic Sign Language video processing.