Detection Based Tracking of Unmanned Aerial Vehicles
| Autor: | Hakan Cevikalp, Onur Eker, Hasan Saribas, Bedirhan Uzun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology 010501 environmental sciences Tracking (particle physics) 01 natural sciences Object detection Video tracking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | SIU |
| DOI: | 10.1109/siu.2019.8806391 |
| Popis: | Object tracking is one of the fundamental problems of computer vision, which has many difficulties such as fast camera motion, occlusion and similar objects. Today, small and lightweight single board computers with very high processing power have been developed. Real-time processing of the computer vision applications on unmanned aerial vehicles has become possible with the integration of such single board computers within UAVs. In this study, a hybrid method is developed to detect and track UAVs by another UAV. A deep learning based approach which is one of the fastest and most accurate method in the literature, YOLOv3 and YOLOv3-Tiny (You Only Look Once), are utilized to detect the UAV at the beginning of the video and when tracking of the UAV is failed. Kernelized Correlation Filter (KCF) is used for real time tracking purpose of the detected UAVs. A dataset is created that consists different UAVs to train and test YOLOv3. Performance of the proposed methods are evaluated on this dataset. |
| Databáze: | OpenAIRE |
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