论文标题
在社交媒体图像中识别单个狗
Identifying Individual Dogs in Social Media Images
论文作者
论文摘要
我们介绍了一项初步研究的结果，该研究重点是开发视觉AI解决方案，能够识别社交媒体上发生的无约束（野生）图像中的单个狗。 此处描述的工作是与Pet2Net共同项目的一部分，Pet2Net是一个专注于宠物及其所有者的社交网络。为了检测和识别单个狗，我们将转移学习和对象检测方法分别结合了v3和SSD Inception V2体系结构，并使用包含用户上传到PET2NET平台的真实数据的新数据集评估了提议的管道。我们表明，它可以在识别单个狗方面达到94.59％的准确性。我们的方法的设计是简单的，并且是在上传到Pet2Net平台上所有图像上轻松部署的目标。 一种纯粹的视觉方法来识别图像中的狗，将增强旨在寻找失落狗的PET2NET功能，并构成了未来工作的基础，该工作专注于识别狗之间的社会关系，这是无法从平台收集的其他数据中推断出来的。
We present the results of an initial study focused on developing a visual AI solution able to recognize individual dogs in unconstrained (wild) images occurring on social media. The work described here is part of joint project done with Pet2Net, a social network focused on pets and their owners. In order to detect and recognize individual dogs we combine transfer learning and object detection approaches on Inception v3 and SSD Inception v2 architectures respectively and evaluate the proposed pipeline using a new data set containing real data that the users uploaded to Pet2Net platform. We show that it can achieve 94.59% accuracy in identifying individual dogs. Our approach has been designed with simplicity in mind and the goal of easy deployment on all the images uploaded to Pet2Net platform. A purely visual approach to identifying dogs in images, will enhance Pet2Net features aimed at finding lost dogs, as well as form the basis of future work focused on identifying social relationships between dogs, which cannot be inferred from other data collected by the platform.
