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The integration of artificial intelligence (AI) into daily life has become increasingly popular. However, AI applications typically rely on powerful hardware or client-server architectures, where computational tasks are offloaded to a server and present challenges in production settings or when network latency occurs. Consequently, implementing AI on compact hardware devices is highly desirable and practical. In this paper, we present the steps to perform onboarding for the ESP32-S3-EYE. Furthermore, we apply an adaptive threshold in the motion detection algorithm, leveraging the limited resources of the board. The results demonstrate that the board operates stably after being connected, and experimental outcomes show the effectiveness of the adaptive threshold compared to previous traditional motion detection techniques, maintaining stable performance.

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Onboarding Process from Power-Up to Motion Detection Using ESP32-EYE: A Quick and Comprehensive Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Tho Nguyen,
  • Long Le,
  • Binh Nguyen,
  • Trung Nguyen,
  • Nhan Tran,
  • Thien Pham,
  • Tho Quan

摘要

The integration of artificial intelligence (AI) into daily life has become increasingly popular. However, AI applications typically rely on powerful hardware or client-server architectures, where computational tasks are offloaded to a server and present challenges in production settings or when network latency occurs. Consequently, implementing AI on compact hardware devices is highly desirable and practical. In this paper, we present the steps to perform onboarding for the ESP32-S3-EYE. Furthermore, we apply an adaptive threshold in the motion detection algorithm, leveraging the limited resources of the board. The results demonstrate that the board operates stably after being connected, and experimental outcomes show the effectiveness of the adaptive threshold compared to previous traditional motion detection techniques, maintaining stable performance.