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.

The declining population of black rhinoceroses in Tsavo West national park, Kenya, has served as the driving force behind Project Ngulia, with Ngulia serving as an enclosed area within the park. As of now, the area is equipped with multiple cameras connected to a system that automatically classify animals and humans. This thesis aims to investigate the suitability of the Insta360 One X2 camera acting as a virtual watch tower for capturing and streaming 360° images. This will work in real-time, providing a remote surveillance experience for the park rangers thereby optimizing their work. A system was implemented to create a efficient workflow, which includes stitching of the 360° images, file transfer protocol for image transmission and storage, as well as socket programming to facilitate port monitoring and communication. Additionally, the compat- ibility of two single board computers, LattePanda and Rock 4 SE, with the implemented system was evaluated. User experience methods as field studies, workshops and a user interview were also performed. The work has been developed in Sweden, resulting in limited availability for testing at the target location during the initial months. The outcome was a both locally and remotely working system, together with LattePanda, capturing images of the waterhole in Ngulia. However, because of the conclusions drawn regarding the power supply and the lack of essential functions in the 360° camera, the system was taken home for further research. Propositions is presented regarding future work, some being that the projects within Ngulia team may collaborate to enhance hardware efficiency and explore the utilization of 360° images in educational and entertainment contexts.