Articulated models for human motion analysis
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Summary
This dissertation presents several approaches for motion analysis that address the problem of pose inference, action recognition and temporal clustering of human motion, and proposes a method to cluster human motion sequences into distinct behaviors, without a priori knowledge of the number of actions in the sequence.
- Type
- dissertation
- Published
- 2012-12-10
- Cited by
- 0
- References
- 150
- Access
- Open access
- OpenAlex
- https://openalex.org/W816730136
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:126934635
Keywords
Computer science, Artificial intelligence, Motion (physics), Motion analysis, Structure from motion
References
- Understanding Human Motion: A Historic Review
- Optimization and Filtering for Human Motion Capture
- Virtual view appearance representation for human motion analysis in multi-view environments
- Segmenting Motion Capture Data into Distinct Behaviors
- A Study of Parts-Based Object Class Detection Using Complete Graphs
- A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns
- Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion
- Dynamics from multivariate time series
- Predicting Articulated Human Motion from Spatial Processes
- Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
- Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications
- A global geometric framework for nonlinear dimensionality reduction.
- Learning a hierarchy of discriminative space-time neighborhood features for human action recognition
- Simultaneous Pose Estimation of Multiple People using Multiple-View Cues with Hierarchical Sampling
- Articulated Body Motion Capture by Stochastic Search
- Practical method for determining the minimum embedding dimension of a scalar time series
- A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
- Coupled Visual and Kinematic Manifold Models for Tracking
- Hierarchical Grouping to Optimize an Objective Function
- Learning Generative Models for Multi-Activity Body Pose Estimation
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