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영상 기반 강아지의 이상 행동 탐지

Camera-based Dog Unwanted Behavior Detection

  • 오스만 (고려대학교 컴퓨터정보학과) ;
  • 이종욱 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 박대희 (고려대학교 컴퓨터융합소프트웨어학과) ;
  • 정용화 (고려대학교 컴퓨터융합소프트웨어학과)
  • Atif, Othmane (Dept. of Computer Information Science, Korea University) ;
  • Lee, Jonguk (Dept. of Computer Convergence Software, Korea University) ;
  • Park, Daehee (Dept. of Computer Convergence Software, Korea University) ;
  • Chung, Yongwha (Dept. of Computer Convergence Software, Korea University)
  • 발행 : 2019.05.10

초록

The recent increase in single-person households and family income has led to an increase in the number of pet owners. However, due to the owners' difficulty to communicate with them for 24 hours, pets, and especially dogs, tend to display unwanted behavior that can be harmful to themselves and their environment when left alone. Therefore, detecting those behaviors when the owner is absent is necessary to suppress them and prevent any damage. In this paper, we propose a camera-based system that detects a set of normal and unwanted behaviors using deep learning algorithms to monitor dogs when left alone at home. The frames collected from the camera are arranged into sequences of RGB frames and their corresponding optical flow sequences, and then features are extracted from each data flow using pre-trained VGG-16 models. The extracted features from each sequence are concatenated and input to a bi-directional LSTM network that classifies the dog action into one of the targeted classes. The experimental results show that our method achieves a good performance exceeding 0.9 in precision, recall and f-1 score.

키워드

과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A3B07044938).