用图像识别技术追踪野生流浪猫，提升保护澳洲野生动物的效率
What cat is that? A re-id model for feral cats
- 改造老虎重识别模型，适配流浪猫图像特征
- 达到mAP 0.86、rank-1准确率0.95的优异性能
- 适用于野生动物监测人员和生态研究者
流浪猫对澳大利亚野生动物造成严重破坏，是全球最危险的入侵物种之一。因此，密切监控这些猫至关重要。本文探索了多种计算机视觉方法，构建可用于野外识别个体流浪猫的重识别（re-ID）模型。核心方法是将原本用于阿穆尔虎重识别的部件-姿态引导网络（PPGNet）进行改进，形成专为流浪猫设计的PPGNet-Cat模型，并引入ArcFace等对比学习损失函数。实验表明，该模型在识别流浪猫方面表现卓越，平均精度均值（mAP）达0.86，排名1准确率达0.95。结果证明PPGNet-Cat在重识别领域具有竞争力。
原文摘要 · Abstract (English)
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.
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