• Author: Loghinescu Nicu Adrian
• Email: nicu.loghinescu@stud.fim.upb.ro
• Master: ACES

The purpose of this project is to monitor specific behavior of your house pets. ESP32-CAM boards with cameras and motion detectors will be placed near areas of interest such as the pet feeder and will send a picture to a firebase server whenever the motion sensor is triggered. The server will use a ML model to recognize your specific pets and save specific data to a database, which can be used to track the pet's behavior.

• AI Thinker ESP32-CAM - based on the ESP-32S module that integrates WIFI and Bluetooth
• OV2640 Camera - supports jpeg image output format
• PIR HC-SR501 Sensor - motion sensor based on the Passive Infrared technology
• FT232RL USB to Serial Converter - for programming the ESP32-CAM

• Firebase
  • Cloud functions - Used as a HTTP server
  • Realtime database - For saving timestamps regarding pet detection
  • Firebase Hosting - Simple Web interface for monitoring pet behavior and trying different ML models
• Google Teachable Machine - Classifier generator from Google that outputs Tensorflow models
• TensorflowJS - Tensorflow library for Javascript, will be used in the Cloud functions
• Arduino IDE alongside the required libraries for ESP32, HTTP client, WiFi, Firebase client and video camera

Shortly, the architecture has classic client-server structure, where the esp32-cam is the http client and firebase the server. The other software and harware components are built on top of these two.

• After programming the ESP32 and deploying the firebase code, the user will need to upload a TensorflowJS model in order to detect their house pets.
• Google's Teachable Machine provides an easy way to generate classifiers for an average user. All the end user has to do is to provide pictures and select the desired cathegory
• Google authomatically stores the model and provides a link to it. After the model has been generated, the user should insert the link in the Web interface that we provide with firebase hosting.

• We will now refer to the ESP32-CAM as the HTTP client and to the cloud functions as the server.
• The client will permanently check the input pin from the motion detector. Once motion is detected, a picture is taken and sent to the server.
• The server will load the model, if it's not already loaded, make a prediction on the received image and send the result back to the client.
• If the HTTP exchange was successful, the client will send the prediction result alongside a timestamp in the UNIX format.

• The web interface is completely based on firebase hosting.
• The main page will present a textbox in order to provide a link to a new model and a playground area where you can drag and drop pictures in order to test the model.
• It will also provide an interface for visualizing the data stored in the realtime database by the esp32.