Implementation of Target Tracking Methods on Images Taken from Unmanned Aerial Vehicles

• Published in: 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
• Main Authors: Eriş H., Çevik U.
• Other Authors: Çukurova Üniversitesi
• Format: Conference Object
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2019
• Subjects: convolutional neural network; deep learning; image analysis; neural network; object classification; target tracking; sami
• Online Access: https://hdl.handle.net/20.500.12605/16164; https://doi.org/10.1109/SAMI.2019.8782768

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 - mAP. © 2019 IEEE.