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Monitoring Human-Wildlife Interactions in National Parks with Crowdsourced Data and Deep Learning

In: Information and Communication Technologies in Tourism 2022

Author

Listed:
  • Bing Pan

    (The Pennsylvania State University)

  • Virinchi Savanapelli

    (Indian Institute of Technology)

  • Abhishek Shukla

    (Indian Institute of Technology)

  • Junjun Yin

    (The Pennsylvania State University)

Abstract

This short paper summarizes the first research stage for applying deep learning techniques to capture human-wildlife interactions in national parks from crowd-sourced data. The results from objection detection, image captioning, and distance calculation are reported. We were able to categorize animal types, summarize visitor behaviors in the pictures, and calculate distances between visitors and animals with different levels of accuracy. Future development will focus on getting more training data and field experiments to collect ground truth on animal types and distances to animals. More in-depth manual coding is needed to categorize visitor behavior into acceptable and unacceptable ones.

Suggested Citation

  • Bing Pan & Virinchi Savanapelli & Abhishek Shukla & Junjun Yin, 2022. "Monitoring Human-Wildlife Interactions in National Parks with Crowdsourced Data and Deep Learning," Springer Books, in: Jason L. Stienmetz & Berta Ferrer-Rosell & David Massimo (ed.), Information and Communication Technologies in Tourism 2022, pages 492-497, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-94751-4_46
    DOI: 10.1007/978-3-030-94751-4_46
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