Pet Companion System Based on Image Recognition

Sun, Zhe; Feng, Wei; Jin, Jintao; Fu, Zhang; Yang, Guanhao; Lei, Qujiang; Deng, Zhonghui; Li, Xiuhao

doi:10.1109/iccar52225.2021.9463484

Cited by 1 publication

(2 citation statements)

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“…It is also worth citing work of Reference 11, in which a mobile robot interacts with a dog, and the applied pet behavior analysis module detects three different behaviors, depending on the distance from the robot. In addition, you can also find preliminary studies for creating a companion for a dog left at home 37 . In this context, it is worth considering that from a machine learning (ML‐DNN) perspective, there is only one publicly available dataset, namely the Stanford Dog Breed Data Set, 38,39 which focuses on the task of breed identification.…”

Section: Dogs and Emotionsmentioning

confidence: 99%

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Categorization of emotions in dog behavior based on the deep neural network

Kowalczuk

Czubenko

Żmuda‐Trzebiatowska

2022

Computational Intelligence

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The aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular breed makes it easier to experiment and recognize the emotional behavior of dogs. To broaden our conclusions, in our research study we compare our system with other systems of different architectures. In addition, we also use modern transfer learning with augmentation and data normalization techniques. The results show that VGG16 and VGG19 are the most suitable backbone networks. Therefore, a certain deep neural network, named mVGG16, based on the suboptimal VGG16 has been created, trained and fine-tuned with transfer (without augmentation and normalization). The developed system is then tested against an internal test dataset. In addition, to show the robustness of the system, a set of external data outside the breed is also taken into account. Being able to detect unsafe dog behavior and rely on a generalization for other breeds is worth popularizing. Equally important are the possible applications of the system 2116

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Section: Dogs and Emotionsmentioning

confidence: 99%

“…In addition, you can also find preliminary studies for creating a companion for a dog left at home. 37 In this context, it is worth considering that from a machine learning (ML-DNN) perspective, there is only one publicly available dataset, namely the Stanford Dog Breed Data Set, 38,39 which focuses on the task of breed identification.…”

Section: Similar Workmentioning

confidence: 99%