Low-Latency Detection and Tracking of Aircraft in Very High-Resolution Video Feeds

• Main Author: Mathiesen, Jarle
• Format: Others
• Language: English
• Published: Linköpings universitet, Interaktiva och kognitiva system 2018
• Subjects: tracking; object tracking; kalman filter; deep learning; remote tower; convolutional neural network; cnn; real-time; Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148848

Applying machine learning techniques for real-time detection and tracking of objects in very high-resolution video is a problem that has not been extensively studied. In this thesis, the practical uses of object detection for airport remote towers are explored. We present a Kalman filter-based tracking framework for low-latency aircraft tracking in very high-resolution video streams. The object detector was trained and tested on a dataset containing 3000 labelled images of aircrafts taken at Swedish airports, reaching an mAP of 90.91% with an average IoU of 89.05% on the test set. The tracker was benchmarked on remote tower video footage from Örnsköldsvik and Sundsvall using slightly modified variants of the MOT-CLEAR and ID metrics for multiple object trackers, obtaining an IDF1 score of 91.9%, and a MOTA score of 83.3%. The prototype runs the tracking pipeline on seven high resolution cameras simultaneously at 10 Hz on a single thread, suggesting large potential speed gains being attainable through parallelization.