Use of Thermal Imagery for Robust Moving Object Detection

• Main Author: Bergenroth, Hannah
• Format: Others
• Language: English
• Published: Linköpings universitet, Medie- och Informationsteknik 2021
• Subjects: Moving object detection; background subtraction; convolutional neural network; image fusion; Media and Communication Technology; Medieteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177888

This work proposes a system that utilizes both infrared and visual imagery to create a more robust object detection and classification system. The system consists of two main parts: a moving object detector and a target classifier. The first stage detects moving objects in visible and infrared spectrum using background subtraction based on Gaussian Mixture Models. Low-level fusion is performed to combine the foreground regions in the respective domain. For the second stage, a Convolutional Neural Network (CNN), pre-trained on the ImageNet dataset is used to classify the detected targets into one of the pre-defined classes; human and vehicle. The performance of the proposed object detector is evaluated using multiple video streams recorded in different areas and under various weather conditions, which form a broad basis for testing the suggested method. The accuracy of the classifier is evaluated from experimentally generated images from the moving object detection stage supplemented with publicly available CIFAR-10 and CIFAR-100 datasets. The low-level fusion method shows to be more effective than using either domain separately in terms of detection results. === <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>