Computer Vision for Camera Trap Footage : Comparing classification with object detection

• Main Author: Örn, Fredrik
• Format: Bachelor Thesis
• Language: English
• Published: Uppsala universitet, Avdelningen för visuell information och interaktion 2021
• Subjects: computer vision; camera traps; classification; object detection; neural networks; artificial intelligence; machine learning; datorseende; kamerafällor; klassificering; detektering; neurala nätverk; artificiell intelligens; maskininlärning; Computer Vision and Robotics (Autonomous Systems); Datorseende och robotik (autonoma system); Arctic; Climate change
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447482

Monitoring wildlife is of great interest to ecologists and is arguably even more important in the Arctic, the region in focus for the research network INTERACT, where the effects of climate change are greater than on the rest of the planet. This master thesis studies how artificial intelligence (AI) and computer vision can be used together with camera traps to achieve an effective way to monitor populations. The study uses an image data set, containing both humans and animals. The images were taken by camera traps from ECN Cairngorms, a station in the INTERACT network. The goal of the project is to classify these images into one of three categories: "Empty", "Animal" and "Human". Three different methods are compared, a DenseNet201 classifier, a YOLOv3 object detector, and the pre-trained MegaDetector, developed by Microsoft. No sufficient results were achieved with the classifier, but YOLOv3 performed well on human detection, with an average precision (AP) of 0.8 on both training and validation data. The animal detections for YOLOv3 did not reach an as high AP and this was likely because of the smaller amount of training examples. The best results were achieved by MegaDetector in combination with an added method to determine if the detected animals were dogs, reaching an average precision of 0.85 for animals and 0.99 for humans. This is the method that is recommended for future use, but there is potential to improve all the models and reach even more impressive results.Teknisk-naturvetenskapliga