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Cat Behavior Pattern Analysis and Disease Prediction System of Home CCTV Images using AI

AI를 이용한 홈CCTV 영상의 반려묘 행동 패턴 분석 및 질병 예측 시스템 연구

  • Han, Su-yeon (Department of Convergence Engineering, Hoseo Graduate School of Venture) ;
  • Park, Dea-Woo (Department of Convergence Engineering, Hoseo Graduate School of Venture)
  • Received : 2022.07.30
  • Accepted : 2022.08.14
  • Published : 2022.09.30

Abstract

Cats have strong wildness so they have a characteristic of hiding diseases well. The disease may have already worsened when the guardian finds out that the cat has a disease. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia, polyuria, and frequent urination more quickly. In this paper, 1) Efficient version of DeepLabCut for pose estimation, 2) YOLO v4 for object detection, 3) LSTM is used for behavior prediction, and 4) BoT-SORT is used for object tracking running on an artificial intelligence device. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the server system.

반려동물 중 반려묘의 비중이 2012년 이후 연평균 25.4%의 증가율을 보이며 증가하는 추세이다. 고양이는 강아지에 비해 야생성이 강하게 남아있기 때문에 질병이 생기면 잘 숨기는 특성이 있다. 보호자가 반려묘가 질병이 있음을 알게 되었을 때는 병이 이미 악화되어진 상태일 수 있다. 반려묘의 식욕부진(식사회피), 구토, 설사, 다음, 다뇨 등과 같은 현상은 당뇨, 갑상선기능항진증, 신부전증, 범백혈구감소증 등 고양이 질병 시 나타나는 증상 중 일부이다. 반려묘의 다뇨(소변 양이 많음), 다음(물 많이 마심), 빈뇨(소변을 자주 봄) 현상을 보호자가 보다 빨리 알아차릴 수 있다면 반려묘의 질병 치료에 크게 도움이 될 것이다. 본 논문에서는 인공지능 디바이스에서 작동하는 1) 자세 예측 DeepLabCut의 Efficient 버전, 2) 객체 검출 YOLO v4, 3) 행동 예측 LSTM 4) 객체 추적은 BoT-SORT를 사용한다. 인공지능 기술을 이용하여 홈 CCTV의 영상에서 반려묘의 행동 패턴 분석과 물그릇의 무게 센서를 통해 반려묘의 다음, 다뇨 및 빈뇨를 예측한다. 그리고, 반려묘 행동 패턴 분석을 통해, 질병 예측 및 이상행동 결과를 보호자에게 리포트 하는, 메인 서버시스템과 보호자의 모바일로 전달하는 애플리케이션을 제안한다.

Keywords

References

  1. Y. R. Pandeya and J. Lee, "Domestic Cat Sound Classification Using Transfer Learning," International J ournal of Fuzzy Logic and Intelligent Systems, vol. 18, no. 2, pp. 154-160, Jun. 2018. https://doi.org/10.5391/IJFIS.2018.18.2.154
  2. LG U+, MomCa, accessed Dec 17, 2013 [Internet]. Available: https://www.uplus.co.kr.
  3. Pet services provided by surepetcare [Internet]. Available: https://www.surepetcare.com/en-gb/animo.
  4. R. Girshick, "Fast R-CNN," in Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, pp.1440-1448, 2015.
  5. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. -Y. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," in Proceedings of European conference on computer vision, Amsterdam, The Netherlands, pp. 21-37, 2016.
  6. Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, and H. Ling, "M2det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network," in Proceedings of the AAAI conference on artificial intelligence, Honolulu: HI, USA, vol. 33, no. 1, pp. 9259-9266, 2019.
  7. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-time Object Detection," in IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas: NV, USA, pp. 779-788, 2015.
  8. M. Tan, R. Pang, Q. V. Le, "EfficientDet: Scalable and Efficient Object Detection," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Virtual, pp. 10781-10790, 2020.
  9. Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. Yu, and J. Sun, "Cascaded Pyramid Network for Multi-Person Pose Estimation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognit, Salt Lake City: UT, USA, pp. 7103-7112, 2018.
  10. Z. Cao, G. Hidalgo, T. Simon, S. -E. Wei, and Y. Sheikh, "OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields," in arXiv preprint arXiv:1812.08008, 2018.
  11. A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning," Nature Neuroscience, vol. 21, pp. 1281-1289, Aug. 2018. https://doi.org/10.1038/s41593-018-0209-y
  12. M. Tan and Q. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in Proceedings of the 36th International Conference on Machine Learning, Long Beach: CA, USA, pp. 6105-6114, 2019.
  13. A. Bochkovskiy, C. -Y. Wang, and H. -Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020.
  14. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780. Nov. 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  15. N. Aharon, R. Orfaig, and B. -Z. Bobrovsky, "BoT-SORT: Robust Associations Multi-Pedestrian Tracking," arXiv:2206.14651, 2022
  16. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, "Support vector machines," IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18-28, Jul-Aug. 1998.
  17. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," in Proceedings of the National Academy of Sciences of the United of America, vol. 79, no. 8, pp. 2554-2558, 1982. https://doi.org/10.1073/pnas.79.8.2554
  18. D. A. Reynolds, "Gaussian Mixture Models," Encyclopedia of Biometrics, pp. 659-663, Jan. 2009.