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An image analysis system Design using Arduino sensor and feature point extraction algorithm to prevent intrusion

  • Received : 2021.10.31
  • Accepted : 2021.12.05
  • Published : 2021.12.30

Abstract

In this paper, we studied a system that can efficiently build security management for single-person households using Arduino, ESP32-CAM and PIR sensors, and proposed an Android app with an internet connection. The ESP32-CAM is an Arduino compatible board that supports both Wi-Fi, Bluetooth, and cameras using an ESP32-based processor. The PCB on-board antenna may be used independently, and the sensitivity may be expanded by separately connecting the external antenna. This system has implemented an Arduino-based Unauthorized intrusion system that can significantly help prevent crimes in single-person households using the combination of PIR sensors, Arduino devices, and smartphones. unauthorized intrusion system, showing the connection between Arduino Uno and ESP32-CAM and with smartphone applications. Recently, if daily quarantine is underway around us and it is necessary to verify the identity of visitors, it is expected that it will help maintain a safety net if this system is applied for the purpose of facial recognition and restricting some access. This technology is widely used to verify that the characters in the two images entered into the system are the same or to determine who the characters in the images are most similar to among those previously stored in the internal database. There is an advantage that it may be implemented in a low-power, low-cost environment through image recognition, comparison, feature point extraction, and comparison.

Keywords

References

  1. Gosselin, P.H., Murray, N., Jegou, H., & Perronnin. F. (2014). Revisiting the fishervector for fine-grained classification. Pattern recognition letters, Vol.49, 92-98. https://doi.org/10.1016/j.patrec.2014.06.011
  2. Guo, T., Dong, J., Li, H., & Gao, Y. (2017). Simple convolutional neural network on image classification. IEEE 2nd International Conference on Big Data Analysis (ICBDA), 721-724.
  3. Kang, H. G., Seo, D. S., Lee, B. S., & Kang, M. S. (2017). Applying CEE (CrossEntropyError) to improve performance of Q-Learning algorithm. Korean Journal of Artificial Intelligence, 5(1), 1-9.
  4. Kong, Y. H., & Lee, W. C. (2017). Dynamic Obstacle Avoidance and Optimal Path Finding Algorithm for Mobile Robot Using Q-Learning. Journal of Korean Institute of Information Technology, 15(9), 57-62. https://doi.org/10.14801/jkiit.2017.15.9.57
  5. Li, Y., Zhang, J., Gao, P., Jiang, L., & Chen, M. (2018). Grab Cut Image Segmentation Based on Image Regi. on. IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 311-315,
  6. Steela, K., Birdsong, W., & Reddy, B. Y. (2019). Image classification using Tensorflow. 16th International Conference on Information Technology-New Generations (ITNG 2019), 485-488.
  7. Yoo, W. S., Seo, J. h., Kim, D. H., & Kim, K. H. (2019). Machine scheduling models based on reinforcement Learning for minimizing due date violation and setup change. The Journal of Society for e-Business Studies, 24(3), 19-33. https://www.seeedstudio.com/32-CAM-Development-Board-with-camer-p-3153.html (accessed 2017)