Abstract
Feral cats exert a substantial and detrimental impact on Australian wildlife, placing them among the most dangerous invasive species worldwide. Therefore, closely monitoring these cats is essential labour in minimising their effects. In this context, the potential application of Re-Identification (re-ID) emerges to enhance monitoring activities for these animals, utilising images captured by camera traps. This project explores different CV approaches to create a re-ID model able to identify individual feral cats in the wild. The main approach consists of modifying a part-pose guided network (PPGNet) model, initially used in the re-ID of Amur tigers, to be applicable for feral cats. This adaptation, resulting in PPGNet-Cat, which incorporates specific modifications to suit the characteristics of feral cats images. Additionally, various experiments were conducted, particularly exploring contrastive learning approaches such as ArcFace loss. The main results indicate that PPGNet-Cat excels in identifying feral cats, achieving high performance with a mean Average Precision (mAP) of 0.86 and a rank-1 accuracy of 0.95. These outcomes establish PPGNet-Cat as a competitive model within the realm of re-ID.
Abstract (translated)
野猫在澳大利亚对野生动物造成了严重的负面影响，使其成为世界上最危险的入侵物种之一。因此，在减少其影响方面，密切监测这些动物是至关重要的工作。在此背景下，重新识别（Re-Identification, Re-ID）技术的应用前景被用于增强针对这些动物的监控活动，利用相机陷阱捕捉到的照片进行操作。本项目探讨了不同的计算机视觉（CV）方法来创建一个能够识别野外个体野猫的身份重识别模型。主要的方法是修改最初用于阿穆尔虎身份重新识别的部分姿态引导网络（PPGNet）模型，使其适用于野猫的特征。这一改编产生了名为 PPGNet-Cat 的新版本，通过特定的修改来适应野猫照片的特点。此外，还进行了各种实验，尤其是探索对比学习方法如 ArcFace 损失函数的应用。 主要的结果表明，PPGNet-Cat 在识别野猫方面表现出色，达到了平均精度（mAP）为 0.86 和首名准确率（rank-1 accuracy）为 0.95 的高性能。这些结果确立了 PPGNet-Cat 在身份重识别领域中的竞争力地位。
URL
https://arxiv.org/abs/2507.11575
