@inproceedings{f6f1632ce7604957b4f4bd1dca1db31b,
title = "Real-time vehicle tracking using convolutional neural networks in aerial video",
abstract = "Vehicle tracking based on video images has been widely used in military and civilian fields. The tracking method must robust enough to hand the unexpected situations that may occur during the tracking process. In this paper, a novel vehicle tracking method based on convolutional neural networks (CNNs) is proposed to target the accurate and speed demand of vehicle tracking. The proposed method contains two networks with shared weights. It utilizes the residual block to reduce the train error. Offline training is used to achieve real-time tracking. It also use transfer learning to reduce training time. The experimental results under the real aerial video demonstrate that vehicle tracker achieves an accuracy of 70.8% and the speed of 135fps with GPU. The proposed method is robust enough to handle occlusion and other interference conditions.",
keywords = "Convolutional neural networks, Real-time, Residual Network, Vehicle tracking",
author = "Yu Yang and Chengpo Mu and Ruixin Yang and Yanjie Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",
year = "2019",
month = aug,
doi = "10.1109/SDPC.2019.00052",
language = "English",
series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "251--255",
editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",
booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",
address = "United States",
}