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Abstract Aiming at tackling the problem of the low efficiency of common detection methods and the dynamic background of UAV-based video sequences imagery, this paper proposes a fast detection method for moving targets in UAV video based on the feature extraction. The method mainly includes six steps, i.e., preprocessing, ORB feature extraction, PROSAC feature fine matching, global motion estimation and global motion compensation, initial detection of moving target and morphological post-processing. The results of two video experiments carried by UAVs show that the moving target detection results of the proposed method in this paper are better, the computational efficiency is the highest, and hence this method can meet the requirements of real-time processing.
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Keywords
video sequential imagery
ORB feature
PROSAC fine matching
moving target detection
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Issue Date: 21 July 2021
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[1] |
Mu Y W, Guo C H, Xin Y D. A new interframe difference algorithm for moving target detection[C]// IEEE International Congress on Image and Signal Processing, 2010(1):285-289.
|
[2] |
熊英. 基于背景和帧间差分法的运动目标提取[J]. 计算机时代, 2014(3):38-41.
|
[2] |
Xiong Y. Moving object extraction based on background difference and frame difference method[J]. Computer Era, 2014(3):38-41.
|
[3] |
Kengo M, Takashi S, Shoji Y, et al. Moving-object detection method for moving cameras by merging background subtraction and optical flow methods[C]// IEEE Global Conference on Signal and Information Processing, 2017:383-387.
|
[4] |
谭熊. 基于光流分析的无人机视频运动目标检测与跟踪[D]. 郑州:解放军信息工程大学, 2011.
|
[4] |
Tan X. UAV video moving target detection and tracking based on optical flow analysis[D]. Zhengzhou:Information Engineering University, 2011.
|
[5] |
王朋月. 基于全局运动估计补偿的目标检测算法研究[D]. 北京:北京工业大学, 2018.
|
[5] |
Wang P Y. Research on target detection algorithm based on global motion estimation and compensation[D]. Beijing:Beijing Industry University, 2018.
|
[6] |
卢裕秋, 孙金玉, 马世伟. 基于深度卷积神经网络的运动目标检测方法[J]. 系统仿真学报, 2019, 31(11):2275-2280.
|
[6] |
Lu Y Q, Sun J Y, Ma S W. Moving object detection based on deep convolutional neural network[J]. Journal of System Simulation, 2019, 31(11):2275-2280.
|
[7] |
Ren S, He K, Gir S R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 2015, 39(6):1137-1149.
doi: 10.1109/TPAMI.2016.2577031
url: http://ieeexplore.ieee.org/document/7485869/
|
[8] |
Redmon J, Divvala S, Girshick R, et al. You only look once:Unified,real-time object detection[C]// IEEE Conference on Computer Vision and Pattern Recognition.IEEE, 2016:779-778.
|
[9] |
Liu W, Anguelov D, Erhan D, et al. SSD:Single shot multi-box detector[C]// European Conference on Computer Vision,Amsterdam.IEEE, 2016:21-37.
|
[10] |
任霞. 基于无人机视频的运动目标检测与跟踪方法研究[D]. 成都:电子科技大学, 2020.
|
[10] |
Ren X. Research of detection and tracking based on the moving targets in UAV video[D]. Chengdu:University of Electronic Science and Technology of China, 2020.
|
[11] |
陈挺. 无人机对地运动目标跟踪技术研究[D]. 西安:西北工业大学, 2018.
|
[11] |
Chen T. Moving objects tracking in aerial videos[D]. Xi’an:Northwestern Polytechnical University, 2018.
|
[12] |
董晶, 傅丹, 杨夏. 无人机视频运动目标实时检测及跟踪[J]. 应用光学, 2013, 34(2):255-259.
|
[12] |
Dong J, Fu D, Yang X, et al, Real-time moving object detection and tracking by using UAV videos[J], Applied Optics, 2013, 34(2):255-259.
|
[13] |
Rublee E, Rabaud V, Konolige K, et al. ORB:An efficient alternative to SIFT or SURF[C]// Proceedings of IEEE International Conference on Computer Vision, Washington,USA, 2011:2564-2571.
|
[14] |
Mair E, Hager G D, Burschka D, et al. Adaptive and generic corner detection based on the accelerated segment test[C]// Proceeding of European Conference on Computer Vision,Crete,Greece, 2010, 63(12):183-196.
|
[15] |
Calonder M, Lepetit V, Fua P. BRIEF:Binary robust independent elementary features[C]// Proceeding of European Conference on Computer Vision,Crete,Greece, 2010, 63(14):778-792.
|
[16] |
Fischler M A. Random sample consensus:A paradigm for model fitting with applications to image analysis and automated cartography[J]. Readings in Computer Vision, 1987:726-740.
|
[17] |
Chum O. Matching with PROSAC—Progressive Sample Consensus[J]. Processing of Conference on CVPR, 2005, 1(2):220-226.
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