Computer Science - Robotics, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Systems and Control

The ESP32-CAM is one of the most widely adopted open-source modules for prototyping embedded vision applications. Since its release in 2019, it has gained popularity among both hobbyists and professional developers due to its affordability, versatility, and integrated wireless capabilities. Despite its widespread use, comprehensive documentation of the performance metrics remains limited. This study addresses this gap by collecting and analyzing over six hours of real-time video streaming logs across all supported resolutions of the OV2640 image sensor, tested under five distinct voltage conditions via an HTTP-based WiFi connection. A long standing bug in the official Arduino ESP32 driver, responsible for inaccurate frame rate logging, was fixed. The resulting analysis includes key performance metrics such as instantaneous and average frame rate, total streamed data, transmission count, and internal chip temperature. The influence of varying power levels was evaluated to assess the reliability of the module.
Comment: Full work available at GitHub: Visa