Motion-based Feature Clustering for Articulated Body Tracking
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Summary
This paper shows how to group sparse 3D motion features to structural clusters, describing the rigid elements of articulated body structures, and shows that moving features can be clustered by their local and temporal properties without any additional image information.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 1
- References
- 19
- Access
- Open access
- OpenAlex
- https://openalex.org/W2689175
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:45674442
Keywords
Artificial intelligence, Computer vision, Cluster analysis, Feature (linguistics), Computer science
References
- Computational Studies in the Interpretation of Structure and Motion: Summary and Extension
- Human motion analysis: a review
- A framework for the functional identification of joint centers using markerless motion capture, validation for the hip joint.
- Monocular perception of biological motion-detection and labeling
- A Survey of Computer Vision-Based Human Motion Capture
- Cognitive neuroscience: Neural mechanisms for the recognition of biological movements
- Visual perception of biological motion and a model for its analysis
- Articulated and elastic non-rigid motion: a review
- Unsupervised Learning of Human Motion
- Low Density Feature Point Matching for Articulated Pose Identification
- Good features to track
- A survey of motion analysis from moving light displays
- 3D Marker Data For Motion Recognition
- A survey of advances in vision-based human motion capture and analysis
- Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm
- Perceptual Grouping from Motion Cues Using Tensor Voting in 4-D
- Local and Global Skeleton Fitting Techniques for Optical Motion Capture
- Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm
- Pyramidal implementation of the lucas kanade feature tracker
Cited by
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