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Open AccessArticle

Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring

1
School of Sciences, Southwest Petroleum University, Chengdu 610500, China
2
Institute of Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China
3
Research Center of Mathematical Mechanics, Southwest Petroleum University, Chengdu 610500, China
4
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
5
Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(22), 4855; https://doi.org/10.3390/s19224855
Received: 16 October 2019 / Revised: 1 November 2019 / Accepted: 4 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Sensors and Sensor's Fusion in Autonomous Vehicles)
Many techniques have been developed for computer vision in the past years. Features extraction and matching are the basis of many high-level applications. In this paper, we propose a multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. The features of the reference image are pre-extracted at different levels. The first-level features are used to roughly check the candidate targets and other levels are used for refined matching. With Gaussian weight function introduced, the support of matching features is accumulated to make a final decision. Adaptive neighborhood and principal component analysis are used to improve the description of the feature. Experimental results verify the efficiency and accuracy of the proposed method. View Full-Text
Keywords: feature; tracking; WMSNs; matching; weight; multi-level feature; tracking; WMSNs; matching; weight; multi-level
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MDPI and ACS Style

Zhou, B.; Duan, X.; Ye, D.; Wei, W.; Woźniak, M.; Połap, D.; Damaševičius, R. Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring. Sensors 2019, 19, 4855.

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