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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-148848 |
| Date | January 2018 |
| Creators | Mathiesen, Jarle |
| Publisher | Linköpings universitet, Interaktiva och kognitiva system |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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