Aircraft tracking based on fully conventional network and Kalman filter. Issue 8 (21st May 2019)

• Record Type: Journal Article
• Authors: Yang, Jiachen; Zhao, Weirong; Han, Yurong; Ji, Chunqi; Jiang, Bin; Zheng, Zhihui; Song, Houbing
• Is Part Of: IET image processing. Volume 13:Issue 8(2019)
• Page Start: 1259
• Page End: 1265
• Publication Date: 2019-05-21
• Subjects: object tracking -- aircraft -- learning (artificial intelligence) -- Kalman filters -- target tracking -- object detection -- image filtering -- nonlinear filters -- convolutional neural nets -- video signal processing -- military computing; aircraft tracking -- military reconnaissance -- deep learning -- object tracking -- region‐based fully convolutional networks -- object detection -- Kalman filter -- target object -- extended KF; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5022
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Aircraft tracking is a significant technology for military reconnaissance, but there is no efficient algorithm to solve this particular problem. Recently, research based on deep learning for object tracking has developed rapidly, and the performance is greatly improved compared to the traditional methods, so the authors refer to relevant work and make an improvement on the previous research to improve the performance on aircraft tracking. They first learn the idea from region‐based fully convolutional networks to perform detection on each frame of video. To avoid the target drift due to the failure of object detection on a certain frame, then they employ Kalman filter (KF) and extended KF together to predict the moving trajectory of the target. Beyond that, this method can confine the valid range based on the size of a target object, which increases the speed of detection. This approach can also correct the bounding box on adjacent frames. The steps are not complicated but have an excellent performance. Through the experiment, it is clear that the proposed method is reasonable and more precise.