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Vision-Based Guidance for Tracking Multiple Dynamic Objects

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Abstract

In this paper, we introduce a novel vision-based framework for tracking multiple active objects using guidance laws based on a rendezvous cone method. These guidance laws enable an unmanned aircraft system, equipped with a monocular camera, to continuously observe a set of moving objects within the field of view of its sensor. During the multi-object tracking process, we detect and categorize feature point estimators for controlling the occurrence of occlusions in a comprehensive fashion. Furthermore, we extend our open-source simulation environment and perform a series of simulations to show the efficacy of our proposed approach.

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Data Availability

All simulation data in this study is included in the article. A preliminary version of this paper was presented in ICUAS 2021 [12].

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Funding

This material is based in part upon work supported by the National Science Foundation through grant #IIS-1851817 and a University of Texas at Arlington Research Enhancement Program grant #270079.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, TX, USA

    Kashish Dhal & Animesh Chakravarthy

  2. Department of Computer Science Engineering, University of Texas at Arlington, Arlington, TX, USA

    Pritam Karmokar & William J. Beksi

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  1. Kashish Dhal
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was jointly written by Kashish Dhal and Pritam Karmokar, and all authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript.

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Correspondence to Animesh Chakravarthy.

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Kashish Dhal and Pritam Karmokar contributed equally to this work.

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Dhal, K., Karmokar, P., Chakravarthy, A. et al. Vision-Based Guidance for Tracking Multiple Dynamic Objects. J Intell Robot Syst 105, 66 (2022). https://doi.org/10.1007/s10846-022-01657-6

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