Unsupervised Analysis of Human Gestures
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Human Motion Analysis: A Review
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
Synthesis and acquisition of laban movement analysis qualitative parameters for communicative gestures
Towards a virtual assistant for performers and stage directors
NIME '06 Proceedings of the 2006 conference on New interfaces for musical expression
Computer
Motion segmentation and retrieval for 3D video based on modified shape distribution
EURASIP Journal on Applied Signal Processing
Semantic Segmentation of Motion Capture Using Laban Movement Analysis
IVA '07 Proceedings of the 7th international conference on Intelligent Virtual Agents
Motion segmentation for time-varying mesh sequences based on spherical registration
EURASIP Journal on Applied Signal Processing - 3DTV: Capture, Transmission, and Display of 3D Video
Gesture-Based Human-Computer Interaction and Simulation
Temporal segmentation of 3-D video by histogram-based feature vectors
IEEE Transactions on Circuits and Systems for Video Technology
A group of novel approaches and a toolkit for motion capture data reusing
Multimedia Tools and Applications
Automatic motion segmentation for human motion synthesis
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Human activity language: grounding concepts with a linguistic framework
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Gesture spotting in continuous whole body action sequences using discrete hidden markov models
GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Accurate and efficient gesture spotting via pruning and subgesture reasoning
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Spatial measures between human poses for classification and understanding
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
3D human motion analysis framework for shape similarity and retrieval
Image and Vision Computing
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Complex human motion (e.g. dance) sequences are typically analyzed by segmenting them into shorter motion sequences, called gestures. However, this segmentation process is subjective, and varies considerably from one choreographer to another. Dance sequences also exhibit a large vocabulary of gestures. In this paper, we propose an algorithm called Hierarchical Activity Segmentation. This algorithm employs a dynamic hierarchical layered structure to represent human anatomy, and uses low-level motion parameters to characterize motion in the various layers of this hierarchy, which correspond to different segments of the human body. This characterization is used with a naïve Bayesian classifier to derive choreographer profiles from empirical data that are used to predict how particular choreographers will segment gestures in other motion sequences. When the predictions were tested with a library of 45 3D motion capture sequences (with 185 distinct gestures) created by 5 different choreographers, they were found to be 93.3% accurate.