Advancements, Challenges, and Future Prospects in Vision-Driven Animal Behavior Recognition Technology

DOI

10.2991/978-94-6463-512-6_55How to use a DOI?

Keywords

Animal Behavior Recognition; 3D CNN; C3D

Abstract

Animal behavior provides crucial insights for advancing ecological and animal husbandry research. With the advent of deep learning technologies, the accuracy and efficiency of animal behavior recognition have significantly improved. Initially reliant on manual feature extraction, the field has evolved to incorporate sophisticated deep learning models, transforming the approach to studying animal dynamics. This article reviews the current landscape of animal behavior recognition, discussing the principles, strengths, and limitations of prevalent models. It also examines the application of these models in diverse research contexts, from behavioral ecology to welfare assessments. The transition to automated systems offers nuanced behavioral insights, facilitating real-time monitoring and predictive analytics. Moreover, this paper addresses persistent challenges in animal behavior recognition, such as variability in environmental conditions and species-specific behaviors, proposing innovative solutions that leverage advancements in machine learning and computer vision. By envisioning future applications, this review underscores the potential of deep learning to revolutionize the understanding of animal behaviors, enhancing both animal welfare and ecological research. This comprehensive analysis not only charts a path forward for the field but also catalyzes new methodologies in animal behavior studies.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guanzhen Li
PY  - 2024
DA  - 2024/09/23
TI  - Advancements, Challenges, and Future Prospects in Vision-Driven Animal Behavior Recognition Technology
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 522
EP  - 532
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_55
DO  - 10.2991/978-94-6463-512-6_55
ID  - Li2024
ER  -