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Active Object Recognition via Monte Carlo Tree Search

Research output: Chapter in Book/Report/Conference proceedingConference contributionProfessional

Details

Original languageEnglish
Title of host publicationICRA 2015 Workshop: Beyond Geometric Constraints
Subtitle of host publicationPlanning for Solving Complex Tasks, Reducing Uncertainty, and Generating Informative Paths & Policies
Number of pages3
Publication statusPublished - 30 May 2015
Publication typeD3 Professional conference proceedings

Abstract

This paper considers object recognition with a camera, whose viewpoint can be controlled in order to improve the recognition results. The goal is to choose a multi-view camera trajectory in order to minimize the probability of having misclassified objects and incorrect orientation estimates. Instead of using offline dynamic programming, the resulting stochastic optimal control problem is addressed via an online Monte Carlo tree search algorithm, which can handle various constraints and provides exceptional performance in large state spaces. A key insight is to use an active hypothesis testing policy to select camera viewpoints during the rollout stage of the tree search.

Keywords

  • Active classification, Object detection, Monte Carlo methods, Decision-making

Publication forum classification

Field of science, Statistics Finland

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