![]() ![]() Image Output Format: JPEG, BMP, GRAYSCALE.Support for serial port local and remote firmware upgrades (FOTA).Support Smart Config/AirKiss technology.Support OV2640 and OV7670 cameras, built-in flash lamp.Up to 160MHz clock speed, summary computing power up to 600 DMIPS.Low power 32-bit CPU, can also serve the application processor.The smallest 802.11b/g/n Wi-Fi BT SoC module.The ESP32-CAM doesn’t have a USB connector, so to program the module you need an FTDI board. ESP32-CAM module also has several GPIO pins to connect the external hardware. The ESP32-CAM module can be programmed with ESP-IDF or with Arduino IDE. It can be used as a face detection system in offices, schools and other private areas and can also be used as wireless monitoring, QR wireless identification, and many other IoT applications. This ESP32-CAM module can be widely used in various IoT applications. Micro SD card slot can be used to store images taken from the camera or to store files. The AI-Thinker ESP32-CAM module comes with an ESP32-S chip, a very small size OV2640 camera and a micro SD card slot. Face recognition can also be done using Raspberry Pi and Pi camera using OpenCV. Using the ESP32-CAM module we can build a face recognition system without using any complex programming and any extra components. ESP32-CAM is a very small camera module with the ESP32-S chip. In this project, we are going to build a Face Recognition System using ESP32-CAM which will also work as an ESP32-CAM Security system by recognizing the face of unauthorized persons. to identify persons but none of them can detect and recognize the persons in public areas such as airports, retail stores, and railway stations except the Face Recognition System.įace recognition systems can, not only be used for security purposes to recognize the persons in public places but also can be used for attendance purposes in offices and schools. While compilation is completed and the dotted line appears, just press the reset button on the ESP32 CAM board.There are many human identification systems that use signatures, fingerprints, voice, hand geometry, face recognition, etc. Then connect the USB cable to the PC and Hit that upload button by selecting AI Thinker ESP32 CAM Board from the board manager. ![]() You need to connect all the components as shown in the circuit diagram. Now the uploading process of code is simple. So I will uncomment this “ define CAMERA_MODEL_AI_THINKER” line in the code. In this example, I am using ESP32 CAM from Ai thinker. #define SSID1 "xxxxxx"Īfter that, you have to uncomment your camera model. Now inside the code, you just need to enter your Wi-Fi Credentials Like SSID and the Password of your router in the “ home_wifi_multi.h” file. Just download this code from the GitHub repository. I am going to use this code written by techiesms. Source Code: IoT Based Surveillance CCTV Camera using ESP32 CAM In case, if you haven’t read that previous post, I will suggest you read out that tutorial so that you can easily setup Arduino IDE for ESP32 CAM board. So today I am trying to make this accessible over the Internet so that I can stream that live video from anywhere in this world. Note: So this is conversion of my previous project in which I could stream the live video within the local area network. Once programming is done, you can remove it. This put your ESP32 CAM Module into Download Mode. And the most important thing, you need to short the IO0 and GND Pin together. Similarly, connect the Rx to UOT and Tx to UOR Pin. Connect the 5V & GND Pin of FTDI module to 5V & GND of ESP32 CAM. ![]()
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