Research Article
Identification of Wild Animals in Forest Surveillance Cameras
@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
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.
