Implementation of Target Tracking Methods on Images Taken from Unmanned Aerial Vehicles
| Autor: | Halit Eris, Ulus Çevik |
|---|---|
| Přispěvatelé: | Çukurova Üniversitesi |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Scheme (programming language)
Network architecture business.industry Computer science neural network Deep learning Feature extraction Process (computing) convolutional neural network deep learning Object (computer science) Object detection image analysis Task analysis Computer vision object classification Artificial intelligence business target tracking computer computer.programming_language |
| Popis: | 17th IEEE World Symposium on Applied Machine Intelligence and Informatics, SAMI 2019 --24 January 2019 through 26 January 2019 -- -- Traditional object detection algorithms generate proposals and implement feature extraction. Then, a classification algorithm is implemented to label object classes. This process is slow, and the accuracy may not be adequate for UAV's real-time application tasks due to their movement in the air. We specified and practically implemented an object detection and localization scheme on images taken from a UAV, and provided the UAV with an advanced vision. We used YOLOv2 model. The YOLOv2 is a suitable object detection approach based on deep learning, and it presents a network architecture with accurate results in high speed. The object detection and localization were successfully implemented for people, car, and motorcycle classes within the threshold confidence scores. We pre-trained the model on COCO dataset and tested the model with our test images. The confidence scores were higher in altitudes from 5 to 15 meters and the confidence scores varied between %45 - %79 mAP. © 2019 IEEE. |
| Databáze: | OpenAIRE |
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