Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2016

Abstract

Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires new or additional labeling to adapt visual perception. The time is takes for a human to label new data is what we call adaptation latency. High adaptation latency is not simply undesirable but may be infeasible for scenarios with limited labeling time and resources. In this paper, we introduce a labeling framework to the environment perception domain that significantly reduces adaptation latency using unsupervised learning in exchange for a small amount of label noise. Using two real-world datasets we demonstrate the speed of our labeling framework, and its ability to collect environment labels that train high performing multi-concept classifiers. Finally, we demonstrate the relevance of this label collection process for visual perception as it applies to navigation in outdoor environments.

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea , October 9-14

First Page

2784

Last Page

2791

ISBN

9781509037629

Identifier

10.1109/IROS.2016.7759432

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/IROS.2016.7759432

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