Human motion primitive discovery and recognition.
Year: 2017
Type: preprint
Abstract: We present a novel framework for the automatic discovery and recognition of \nhuman motion primitives from motion capture data. Human motion primitives are \ndiscovered by optimizing the 'motion flux', a quantity which depends on the \nmotion of a group of skeletal joints. Models of each primitive category are \ncomputed via non-parametric Bayes methods and recognition is performed based on \ntheir geometric properties. A normalization of the primitives is proposed in \norder to make them invariant with respect to anatomical variations and data \nsampling rate. Using our framework we build a publicly available dataset of \nhuman motion primitives based on motion capture sequences taken from well-known \ndatasets. We expect that our framework, by providing an objective way for \ndiscovering and categorizing human motion, will be a useful tool in numerous \nresearch fields related to Robotics including human inspired motion generation, \nlearning by demonstration, and intuitive human-robot interaction.
Source: arXiv (Cornell University)
Language: English
Topic: Human Motion and Animation
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2756574245