About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24–25, 2023, Proceedings

Research Article

Identification of Wild Animals in Forest Surveillance Cameras

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-66044-3_16,
        author={Prathyusha Dokku and Swapna Mudrakola and Kalyan Kumar Dadi and Nikhitha Akula},
        title={Identification of Wild Animals in Forest Surveillance Cameras},
        proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. Second EAI International Conference, PerSOM 2023, Hyderabad, India, November 24--25, 2023, Proceedings},
        proceedings_a={PERSOM},
        year={2024},
        month={8},
        keywords={Neural Networks VGG Image processing techniques},
        doi={10.1007/978-3-031-66044-3_16}
    }
    
  • Prathyusha Dokku
    Swapna Mudrakola
    Kalyan Kumar Dadi
    Nikhitha Akula
    Year: 2024
    Identification of Wild Animals in Forest Surveillance Cameras
    PERSOM
    Springer
    DOI: 10.1007/978-3-031-66044-3_16
Prathyusha Dokku1,*, Swapna Mudrakola1, Kalyan Kumar Dadi1, Nikhitha Akula1
  • 1: Department of Computer Science and Engineering, Matrusri Engineering College
*Contact email: cse19733083@matrusri.edu.in

Abstract

In the ever-expanding realm of wildlife conservation and ecological research, the use of automated image classification software has emerged as a valuable tool for extracting crucial insights from camera trap images. However, a persistent challenge lies in the software’s ability to maintain consistent performance and spatial independence for a given image, thus necessitating a solution to enhance its location invariance. The paper introduces an optimized location-invariant camera trap object detector, trained with publicly available image datasets, demonstrating a significant performance improvement with an epoch accuracy of up to 99%. This innovative approach not only addresses the current limitations but also opens avenues for more robust and globally applicable wildlife monitoring solutions, fostering advancements in ecological understanding and conservation efforts.

Keywords
Neural Networks VGG Image processing techniques
Published
2024-08-13
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-66044-3_16
Copyright © 2023–2025 ICST
EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL