Wildlife Detection using Motion History Information Captured by Camera Trap in the Dark

Shogo Yoshida, Yuki Matsuda, Kota Tsubouchi, Hirohiko Suwa, Keiichi Yasumoto: “Wildlife Detection using Motion History Information Captured by Camera Trap in the Dark,” 27th International Conference on Distributed Computing and Networking (ICDCN '26 Companion), pp.22-23, 2026.

Abstract

This paper proposes a method to analyze infrared camera trap images captured at night for wildlife monitoring. Infrared images are typically noisy, making daytime image analysis ineffective. Our approach extracts motion information from continuous frames to enhance classification accuracy. Experiments using real nocturnal data demonstrate that the proposed method outperforms existing models that analyze single images, achieving higher efficiency and accuracy in wildlife detection under low-light and noisy conditions.

Links

DOI: https://doi.org/10.1145/3737611.3776961

PDF: https://cocolab.jp/publication/files/202601_ICDCN_Yoshida.pdf

BibTeX

code:references.bib

@inproceedings{bib:yoshida_wildAnimal_ICDCN2026,

author={Yoshida, Shogo and Matsuda, Yuki and Tsubouchi, Kota and Suwa, Hirohiko and Yasumoto, Keiichi},

title={Wildlife Detection using Motion History Information Captured by Camera Trap in the Dark},

booktitle={27th International Conference on Distributed Computing and Networking (ICDCN '26 Companion)},

pages={22--23},

year={2026},

doi={10.1145/3737611.3776961},