DOI QR코드

DOI QR Code

저가 카메라를 이용한 스마트 장난감 게임을 위한 모형 자동차 인식

Recognition of Model Cars Using Low-Cost Camera in Smart Toy Games

  • 투고 : 2023.09.06
  • 심사 : 2024.01.19
  • 발행 : 2024.02.28

초록

Recently, there has been a growing interest in integrating physical toys into video gaming within the game content business. This paper introduces a novel method that leverages low-cost camera as an alternative to using sensor attachments to meet this rising demand. We address the limitations associated with low-cost cameras and propose an optical design tailored to the specific environment of model car recognition. We overcome the inherent limitations of low-cost cameras by proposing an optical design specifically tailored for model car recognition. This approach primarily focuses on recognizing the underside of the car and addresses the challenges associated with this particular perspective. Our method employs a transfer learning model that is specifically trained for this task. We have achieved a 100% recognition rate, highlighting the importance of collecting data under various camera exposures. This paper serves as a valuable case study for incorporating low-cost cameras into vision systems.

키워드

과제정보

이 연구는 금오공과대학교 대학 학술연구비로 지원되었음 (2021).

참고문헌

  1. 김한결, K-스마트 토이 '루미' 중동시장 진출, 환경미디어, 2020. 
  2. A. Krizhevsky, I. Sutskever, G. Hinton, "Imagenet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems, Vol. 25, 2012. 
  3. K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-scale Image Recognition," 3rd International Conference on Learning Representations, 2015. 
  4. C. Szegedy, W. Liu, Y. jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going Deeper with Convolutions," Proceedings of the IEEE Conference on Computer Vand Pattern Recognition, pp. 1-9, 2015. 
  5. K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016. 
  6. A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, "Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications," CoRR, Vol. abs/1704.04861, 2017. 
  7. M. Tan, Q. Le, "Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks," International Conference on Machine Learning, pp. 6105-6114, 2019. 
  8. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, "Attention is All you Need," Advances in Neural Information Processing Systems, Vol. 30, 2017. 
  9. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," International Conference on Learning Representations, 2021. 
  10. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei, "Imagenet: A Large-scale Hierarchical Image Database," 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009. 
  11. S. J. Pan, Q. Yang, "A Survey on Transfer Learning," IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 10, pp. 1345-1359, 2009.  https://doi.org/10.1109/TKDE.2009.191
  12. A. Hore, D. Ziou, "Image Quality Metrics: PSNR vs. SSIM," 2010 20th International Conference on Pattern Recognition, pp. 2366-2369, 2010. 
  13. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," JMLR, pp. 1929-1958, 2014. 
  14. D. K. Choi, C. H. Han, "Illuminance Dynamic Range Expansion using Gamma & Multi-Point Knee for Smart Phone Camera," IEMEK J. Embed. Sys. Appl., Vol. 8, No. 1, pp. 43-50, 2013 (in Korean). 
  15. S. W. Park, K. R. Cha, "Dynamic Range Reconstruction Algorithm for Smart Phone Camera Pulse Measurement Robust to Light Condition," IEMEK J. Embed. Sys. Appl., Vol. 10, No. 1, pp. 1-6, 2015 (in Korean).  https://doi.org/10.14372/IEMEK.2015.10.1.1
  16. D. P. Kingma, J. Ba, "Adam: A Method for Stochastic Optimization," ICLR, pp. 1-15, 2015.