Active Object Recognition via Monte Carlo Tree Search

Mikko Lauri, Nikolay Atanasov, George Pappas, Risto Ritala

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

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    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.
    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

    Keywords

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

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