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Abstract

We address the task of inferring the future actions of peo- ple from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the eect of the physical environment on the choice of human actions. This is ac- complished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unied model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individu- als. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.

Keywords

activity forecasting, inverse optimal control

Notes

Associated Center(s) / Consortia: Quality of Life Technology Center

Text Reference

Kris Kitani, Brian D. Ziebart, J. Andrew (Drew) Bagnell, and Martial Hebert, "Activity Forecasting," European Conference on Computer Vision, October, 2012.

BibTeX Reference

@inproceedings{Kitani_2012_7250,
   author = "Kris Kitani and Brian D. Ziebart and J. Andrew (Drew) Bagnell and Martial Hebert",
   title = "Activity Forecasting",
   booktitle = "European Conference on Computer Vision",
   publisher = "Springer",
   month = "October",
   year = "2012",
}

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