UAV-vision-based moving target tracking scheme in electric scenario

被引：0
|
作者：
Feng X. [1 ]
Du M. [1 ]
Xiang X. [1 ]
Qian J. [1 ]
Zhang M. [1 ]
机构：
[1] Hangzhou Power Supply Company of Zhejiang Power Co. Ltd. of State Grid Corporation of China, Hangzhou
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2022年 / 48卷 / 04期
关键词：
Deep learning; Electric scenario; Target detection; Target tracking; UAV vision;
D O I：
10.13700/j.bh.1001-5965.2020.0613
中图分类号：
学科分类号：
摘要：
With the rapid development of artificial intelligence technology, the moving target tracking technology for the power system has gradually attracted researchers' attention. Although the existing methods have achieved great success, most of them are based on the fixed camera surveillance video recording, which cannot track the moving target flexibly. When the moving object leaves the camera's field of view, there is a problem of losing the moving object. In light of this, we propose a moving target tracking scheme based on UAV vision (MTTS_UAV) in electric scenario. In particular, to ensure the real-time feature, we combine the improved target tracking algorithm and target detection algorithm to track the hidden dangers. To ensure the accuracy, we introduce two UAV flight control modules: heuristic mode and data-driven mode, so that the UAV's flight speed and direction can be adjusted adaptively according to the target movement. Extensive experiments have been conducted on a real-world dataset of hardhat personnel in real substations, which demonstrate that the average pixel error (APE) and average overlap rate (AOR) are 2.37 and 0.67 respectively and verify the effectiveness of the proposed method. © 2022, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：586 / 594
页数：8
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