Robotics: Science and Systems X

Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs

Yun Jiang, Ashutosh Saxena

Abstract:

For robots, the ability to model human configurations and temporal dynamics is crucial for the task of anticipating future human activities, yet requires conflicting properties: On one hand, we need a detailed high-dimensional description of human configurations to reason about the physical plausibility of the prediction; on the other hand, we need a compact representation to be able to parsimoniously model the relations between the human and the environment. We therefore propose a new model, GP-LCRF, which admits both the high-dimensional and low-dimensional representation of humans. It assumes that the high-dimensional representation is generated from a latent variable corresponding to its low-dimensional representation using a Gaussian process. The generative process not only defines the mapping function between the high- and low-dimensional spaces, but also models a distribution of humans embedded as a potential function in GP-LCRF along with other potentials to jointly model the rich context among humans, objects and the activity. Through extensive experiments on activity anticipation, we show that our GP-LCRF consistently outperforms the state-of-the-art results and reduces the predicted human trajectory error by 11.6%.

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Bibtex:

  
@INPROCEEDINGS{Jiang-RSS-14, 
    AUTHOR    = {Yun Jiang AND Ashutosh Saxena}, 
    TITLE     = {Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2014}, 
    ADDRESS   = {Berkeley, USA}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2014.X.015} 
}