A research field currently advancing is the use of machine learning on camera trap data, yet few explore deep learning for camera traps to be run in real-time. A camera trap has the purpose to capture images of bypassing animals and is traditionally based only on motion detection. This work integrates machine learning on the edge device to also perform object detection. Related research is brought up and model tests are performed with a focus on the trade-off regarding inference speed and model accuracy. Transfer learning is used to utilize pre-trained models and thus reduce training time and the amount of training data. Four models with slightly different architecture are compared to evaluate which model performs best for the use case. The models tested are SSD MobileNet V2, SSD Inception V2, and SSDLite MobileNet V2, SSD MobileNet V2 quantized. Since the client-side usage of the model, the SSD MobileNet V2 was finally selected due to a satisfying trade-off between inference speed and accuracy. Even though it is less accurate in its detections, its ability to detect more images per second makes it outperform the more accurate Inception network in object tracking. A contribution of this work is a light-weight tracking solution using tubelet proposal. This work further discusses the open set recognition problem, where just a few object classes are of interest while many others are present. The subject of open set recognition influences data collection and evaluation tests, it is however left for further work to research how to integrate support for open set recognition in object detection models. The proposed system handles detection, classification, and tracking of animals in the African savannah, and has potential for real usage as it produces meaningful events

The prominence of Edge Machine Learning is increasing swiftly as the performance of microcontrollers continues to improve. By deploying object detection and classification models on edge devices with camera sensors, it becomes possible to locate and identify objects in their vicinity. This technology finds valuable applications in wildlife conservation, particularly in camera traps used in African sanctuaries, and specifically in the Ngulia sanctuary, to monitor endangered species and provide early warnings for potential intruders. When an animal crosses the path of a an edge device equipped with a camera sensor, an image is captured, and the animal's presence and identity are subsequently determined. The performance of three distinct object detection models: SSD MobileNetV2, FOMO MobileNetV2, and YOLOv5 is evaluated. Furthermore, the compatibility of these models with three different microcontrollers ESP32 TimerCam from M5Stack, Sony Spresence, and LILYGO T-Camera S3 ESP32-S is explored. The deployment of Over-The-Air updates to edge devices stationed in remote areas is presented. It illustrates how an edge device, initially deployed with a model, can collect field data and be iteratively updated using an active learning pipeline. This project evaluates the performance of three different microcontrollers in conjunction with their respective camera sensors. A contribution of this work is a successful field deployment of a LILYGO T-Camera S3 ESP32-S running the FOMO MobileNetV2 model. The data captured by this setup fuels an active learning pipeline that can be iteratively retrain the FOMO MobileNetV2 model and update the LILYGO T-Camera S3 ESP32-S with new firmware through Over-The-Air updates. / Project Ngulia