NDLI: High confidence detection for moving target in aerial video

Content Provider	IET Digital Library
Author	Tian, Yumin Peng, Chenhui Wang, Di Wan, Bo
Abstract	The moving target detection and tracking in aerial video is a challenge task because of its moving background, smaller target sizes, lower resolution and limited onboard computing resources. In this study, a high confidence detection method based on background compensation and three-frame-difference method is designed, which can detect moving objects in a dynamic background accurately. First, the authors use local feature extraction and matching for image registration and demonstrate that speed-up robust feature key points are suitable for the stabilisation task. Then, they estimate the global camera motion parameters using affine transformation which are obtained by the random sample consensus algorithm. Finally, they detect moving object by three-frame-difference method. As the detection results of the frame-difference method generally exists ‘empty’ and noise, in order to select the two higher-quality differential images to perform the logic AND operation, they add image quality assessment to the three-frame-difference method to obtain more accurate moving objects. Moreover, the edge detection algorithm and morphological processing are integrated together to further boost the overall detecting performance. The extensive empirical evaluations on aerial videos demonstrate that the proposed detector is very promising for the various challenging scenarios.
Starting Page	2724
Ending Page	2734
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6388
Journal	IET Image Processing
Publisher Date	2019-06-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Moving Objects Aerial Video Affine Transforms Background Compensation Camera Challenge Task Computer Vision And Image Processing Technique Detecting Performance Detection Result Dynamic Background Edge Detection Edge Detection Algorithm Feature Extraction Global Camera Motion Parameter High Confidence Detection Method Higher-quality Differential Image Image Motion Analysis Image Quality Assessment Image Recognition Image Registration Image Segmentation Limited Onboard Computing ReSource Local Feature Extraction Moving Background Moving Target Detection Object Detection Optical, Image And Video Signal Processing Random Sample Consensus Algorithm Smaller Target Sizes Speed-up Robust Feature Key Point Stabilisation Task Three-frame-difference Method Tracking Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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