Augmented dictionary learning for motion prediction
Name
How_Augmented dictionary.pdf
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3.28 MB
Format
Adobe PDF
Checksum (MD5)
3ab10916c4b007d8345b89e2a7445fd3
Author(s)
Chen, Yu Fan
Liu, Miao
How, Jonathan P
Date Issued
May 2016
Journal
IEEE International Conference on Robotics and Automation, 2016. ICRA '16.
Citation
Chen, Yu Fan, Miao Liu, and Jonathan P. How. “Augmented Dictionary Learning for Motion Prediction.” IEEE, 2016. 2527–2534.
Version
Author's final manuscript
Abstract
Developing accurate models and efficient representations of multivariate trajectories is important for understanding the behavior patterns of mobile agents. This work presents a dictionary learning algorithm for developing a part-based trajectory representation, which combines merits of the existing Markovian-based and clustering-based approaches. In particular, this work presents the augmented semi-nonnegative sparse coding (ASNSC) algorithm for solving a constrained dictionary learning problem, and shows that the proposed method would converge to a local optimum given a convexity condition. We consider a trajectory modeling application, in which the learned dictionary atoms correspond to local motion patterns. Classical semi-nonnegative sparse coding approaches would add dictionary atoms with opposite signs to reduce the representational error, which can lead to learning noisy dictionary atoms that correspond poorly to local motion patterns. ASNSC addresses this problem and learns a concise set of intuitive motion patterns. ASNSC shows significant improvement over existing trajectory modeling methods in both prediction accuracy and computational time, as revealed by extensive numerical analysis on real datasets.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2016.7487407
