Human action recognition using star skeleton
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
View-Invariant Pose Recognition Using Multilinear Analysis and the Universum
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Recognizing body poses using multilinear analysis and semi-supervised learning
Pattern Recognition Letters
Tracking HoG Descriptors for Gesture Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Multifactor feature extraction for human movement recognition
Computer Vision and Image Understanding
Dimension reduction in 3d gesture recognition using meshless parameterization
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Immersive virtual aquarium with real-walking navigation
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
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We present a method for describing arbitrary human posture as a combination of basic postures. This decomposition allows for recognition of a larger number of postures and gestures from a small set of elementary postures called atoms. We propose a modified version of the matching pursuit algorithm for decomposing an arbitrary input posture into a linear combination of primary and secondary atoms. These atoms are represented through their shape descriptor inferred from the 3D visual-hull of the human body posture. Using an atom-based description of postures increases tremendously the set of recognizable postures while reducing the required training data set. A gesture recognition system based on the atom decomposition and Hidden Markov Model (HMM) is also described. Instead of representing gestures as HMM transition of postures, we separate the description of gestures as two HMMs, each describing the transition of Primary/Secondary atoms; thus greatly reducing the size of state space of HMM. We illustrate the proposed approach for posture and gesture recognition method on a set of video streams