Robotics: Science and Systems X
Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs
Yun Jiang, Ashutosh SaxenaAbstract:
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%.
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} }