Abstract
Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offered new methodological perspectives through automatization of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identifications done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2% accuracy and (ii) the creation of an individual recognizer for K{ō}jima island macaques population (YOLOv8n model), reaching a 83% accuracy. We also created a K{ō}jima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this innovative approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.
Abstract (translated)
个人识别在生态学和行为学中具有关键作用，尤其是在复杂的社会结构理解中作为工具。然而，传统的识别方法通常涉及入侵的物理标签，对动物和研究人员来说都可能具有破坏性和耗时。近年来，将深度学习应用于研究为自动化完成复杂任务提供了新的方法论视角。越来越多的研究人员利用对象检测和识别技术来实现对视频录像中的个体的识别。这项研究是对通过深度学习开发非侵入性工具进行初步探索，以研究日本狨猴（Macaca fuscata）的面部检测和个体识别。这一研究的主要目标是，利用数据集中的识别结果，自动生成研究人口的社会网络表示。目前的主要结果是有希望：（i）创建了一个日本狨猴面部检测器（Faster-RCNN模型），达到82.2%的准确率；（ii）创建了一个K{ō}jima Island macaques population individual recognizer（YOLOv8n模型），达到83%的准确率。我们还通过传统方法创建了一个K{ō}jima population social network，基于视频中的共同出现。因此，我们为自动生成的网络设立了基准。这些初步结果是对这种创新方法为科学研究界提供跟踪狨猴个体和社交网络的工具具有潜力的有力证明。
URL
https://arxiv.org/abs/2310.06489
