The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking. (11th December 2021)

• Record Type: Journal Article
• Authors: Tang, Cundong; Chen, Li; Wang, Yi; Yang, Wusi; Chen, Rui; Wang, Zhiping
• Other Names: Lv Haibin Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-12-11
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/3097031
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.