A Smart Surveillance System Using Edge-Devices for Wildlife Preservation in Animal Sanctuaries

Abstract: The Internet of Things is a constantly developing field. With advancements of algorithms for object detection and classification for images and videos, the possibilities of what can be made with small and cost efficient edge-devices are increasing. This work presents how camera traps and deep learning can be utilized for surveillance in remote environments, such as animal sanctuaries in the African Savannah. The camera traps connect to a smart surveillance network where images and sensor-data are analysed. The analysis can then be used to produce valuable information, such as the location of endangered animals or unauthorized humans, to park rangers working to protect the wildlife in these animal sanctuaries. Different motion detection algorithms are tested and evaluated based on related research within the subject. The work made in this thesis builds upon two previous theses made within Project Ngulia. The implemented surveillance system in this project consists of camera sensors, a database, a REST API, a classification service, a FTP-server and a web-dashboard for displaying sensor data and resulting images. A contribution of this work is an end-to-end smart surveillance system that can use different camera sources to produce valuable information to stakeholders. The camera software developed in this work is targeting the ESP32 based M5Stack Timer Camera and runs a motion detection algorithm based on Self-Organizing Maps. This improves the selection of data that is fed to the image classifier on the server. This thesis also contributes with an algorithm for doing iterative image classifications that handles the issues of objects taking up small parts of an image, making them harder to classify correctly.