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Vision and Geolocation Data Combination for Precise Human Detection and Tracking in Search and Rescue Operations
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作者 Lygouras Eleftherios 《International Journal of Intelligence Science》 2020年第3期41-64,共24页
In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aeria... In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, </span><span style="font-family:Verdana;">are</span><span style="font-family:Verdana;"> presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented. 展开更多
关键词 Distressed Human Detection Unmanned Aerial Vehicles (Uavs) search and Rescue (SAR) operations Aerial Image Processing Image Processing Algorithms
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Do Search and Selection Operators Play Important Roles in Multi-Objective Evolutionary Algorithms:A Case Study 被引量:1
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作者 Yan Zhen-yu, Kang Li-shan, Lin Guang-ming ,He MeiState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, ChinaSchool of Computer Science, UC, UNSW Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600 AustraliaCapital Bridge Securities Co. ,Ltd, Floor 42, Jinmao Tower, Shanghai 200030, China 《Wuhan University Journal of Natural Sciences》 CAS 2003年第S1期195-201,共7页
Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search an... Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators. 展开更多
关键词 multi-objective evolutionary algorithm convergence property analysis search operator selection operator Markov chain
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Should Search and Rescue Operations Be Free of Charge?
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《Beijing Review》 2011年第18期46-47,共2页
On April 3, 39 teachers and students from the Beijing Institute of Technology (BIT)were trapped on the MaoerMountain in Fangshan District,a suburban area in Beijing. Morethan 300 persons,
关键词 In Should search and Rescue operations Be Free of Charge BE
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Hybrid Krill Herd Algorithm with Vortex Search for Global Numerical Optimization 被引量:2
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作者 YANG Jian WAN Zhongping PENG Zhenhua 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2020年第2期109-117,共9页
In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimizat... In order to solve the problem that the krill herd(KH)algorithm is premature due to the decrease of population diversity,a new hybrid vortex search KH(VSKH)algorithm has been developed to deal with the global optimization problem.The improvement is that a new hybrid vortex search(HVS)operator is added into the updating process of the krill for the purpose of dealing with optimization problems more efficiently.Using 20 benchmark functions for comparison experiments,the results show that the VSKH algorithm has higher accuracy. 展开更多
关键词 krill herd algorithm global optimization problem hybrid vortex search operator(HVS)
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