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A Vision-based Slow-Fast Target-Positioning Framework for Person-Following

Published: 16 May 2020 Publication History

Abstract

The person-following technique has a promising prospect in living and industrial applications, but it encounters several practical issues such as scene changes and pose variations, especially when deployed on a mobile robot with limited computing power. In order to address the challenges in the person-following scenario, this study formulates the visual target-positioning procedure and proposes a Slow-fast Target-Positioning Framework (STPF) to provide robust target positions in real time. Within this framework, the fast branch enables the real-time capability, while the slow branch corrects the cumulative error and improves the robustness. A dataset is collected and setup to evaluate the impact of STPF on the performance of long-term person-following. Extensive experiments demonstrate that STPF reduces 75% interruptions compared to the KCF tracker baseline, and is well adapted to the long-term person-following in real scenarios.

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      cover image ACM Other conferences
      ICCAE 2020: Proceedings of the 2020 12th International Conference on Computer and Automation Engineering
      February 2020
      231 pages
      ISBN:9781450376785
      DOI:10.1145/3384613
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • The University of Western Australia, Department of Electronic Engineering, University of Western Australia
      • Macquarie U., Austarlia
      • University of Technology Sydney

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 16 May 2020

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      Author Tags

      1. Person following
      2. object tracking
      3. visual target-positioning

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