| Citation: | FENG Xue, DU Mengjun, XIANG Xinyu, et al. UAV-vision-based moving target tracking scheme in electric scenario[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(4): 586-594. doi: 10.13700/j.bh.1001-5965.2020.0613(in Chinese)
|
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.
| [1] |
REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL]. (2018-04-08)[2021-11-01]. https://arxiv.org/abs/1804.02767.
|
| [2] |
HENRIQUES J F, RUI C, MARTINS P, et al. High-speed tracking with kernelized correlation filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596.
|
| [3] |
CHO K, MERRIENBOER B V, GÜLÇEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 2014: 1724-1734.
|
| [4] |
VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2001, 1: 511.
|
| [5] |
SUGIMURA D, FUJIMURA T, HAMAMOTO T. Enhanced cascading classifier using multi-scale HOG for pedestrian detection from aerial images[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2016, 30(3): 1655009.
|
| [6] |
DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005: 886-893.
|
| [7] |
ZHU Q, AVIDAN S, YEH M C, et al. Fast human detection using a cascade of histograms of oriented gradients[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2006: 1491-1498.
|
| [8] |
GIRSHICK R. Fast R-CNN[C]//International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 1440-1448.
|
| [9] |
REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
|
| [10] |
LIU W, ANGUELOV D, ERHAN D. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Berlin: Springer, 2016, 9905: 21-37.
|
| [11] |
吴冬梅, 王慧, 李佳. 基于改进Faster RCNN的安全帽检测及身份识别[J]. 信息技术与信息化, 2020(1): 17-20. https://www.cnki.com.cn/Article/CJFDTOTAL-SDDZ202001009.htm
WU D M, WANG H, LI J. Helmet detection and identification based on improved Faster RCNN[J]. Journal of Information Technology and Informatization, 2020(1): 17-20(in Chiense). https://www.cnki.com.cn/Article/CJFDTOTAL-SDDZ202001009.htm
|
| [12] |
董召杰. 基于YOLOv3的电力线关键部件实时检测[J]. 电子测量技术, 2019, 42(43): 173-178. https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL201923029.htm
DONG Z J. Key components of power line based on YOLOv3 real-time detection[J]. Journal of Electronic Measurement Technique, 2019, 42(43): 173-178(in Chiense). https://www.cnki.com.cn/Article/CJFDTOTAL-DZCL201923029.htm
|
| [13] |
XIANG G. Real-time follow up tracking fast moving object with an active camera[C]//2nd Internationl Congress on Image and Signal Processing. Piscataway: IEEE Press, 2009: 2075-2078.
|
| [14] |
ZHANG J E, WANG Y T, CHEN J, et al. A framework of surveillance system using a PTZ camera[C]//IEEE International Conference on Computer Science and Information Technology(ICCSIT). Piscataway: IEEE Press, 2010: 658-662.
|
| [15] |
DONG Q, ZOU Q H. Visual UAV detection method with online feature classification[C]//Proceedings of 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference. Piscataway: IEEE Press, 2017: 464-467.
|
| [16] |
林勇, 秦文静, 戚国庆. 基于机载视觉引导的无人机自主循迹研究[J]. 电子设计工程, 2018, 26(8): 21-24. https://www.cnki.com.cn/Article/CJFDTOTAL-GWDZ201808005.htm
LIN Y, QING W J, QI G Q. Autonomous tracking research of UAV based on airborne visual guidance[J]. Journal of Electronic Design Engineering, 2018, 26(8): 21-24(in Chiense). https://www.cnki.com.cn/Article/CJFDTOTAL-GWDZ201808005.htm
|
| [17] |
XU Z H, ZHANG Y, LI H B, et al. A new shadow tracking method to locate the moving target in SAR imagery based on KCF[C]//Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. Berlin: Springer, 2017: 2661-2669.
|
| [18] |
FAN J X, LI D W, WANG H L, et al. UAV low altitude flight threat perception based on improved SSD and KCF[C]//International Conference on Control and Automation. Piscataway: IEEE Press, 2019: 356-361.
|
| [19] |
徐小超, 严华. 引入目标分块模型的核相关滤波目标追踪算法[J]. 计算机应用, 2020, 40(3): 683-688. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202003013.htm
XU X C, YAN H. A target tracking algorithm based on kernel correlation filter is introduced[J]. Computer Application, 2020, 40(3): 683-688(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY202003013.htm
|
| [20] |
HAN S B, XIE Z J, LI H J, et al. Scale estimation for KCF tracker based on feature fusion[C]//The 4th International Conference on Innovation in Artificial Intelligence. New York: ACM, 2020: 32-36.
|
| [21] |
SIMON M, AMENDE K, KRAUS A, et al. Complexer-YOLO: Real-time 3D object detection and tracking on semantic point clouds[C]//Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE Press, 2019: 1190-1199.
|
| [22] |
ZHANG H B, QIN L F, LI J, et al. Real-time detection method for small traffic signs based on Yolov3[J]. IEEE Access, 2020, PP(99): 1.
|
| [23] |
WU Y, LIM J, YANG M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848.
|
| [24] |
KRISTAN M, MATAS J, LEONARDIS A, et al. The visual object tracking VOT2013 challenge results[C]//IEEE International Conference on Computer Vision Workshops. Piscataway: IEEE Press, 2013: 98-111.
|
| [25] |
KRISTAN M, JIRI M, LEONARDIS A. The visual object tracking VOT2015 challenge results[C]//IEEE International Conference on Computer Vision Workshop. Piscataway: IEEE Press, 2015: 564-586.
|
Figures(2) / Tables(4)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
