The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The ...The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.展开更多
This paper is focused on the task of searching for a specific vehicle that appears in the surveillance networks.Existing methods usually assume the vehicle images are well cropped from the surveillance videos,and then...This paper is focused on the task of searching for a specific vehicle that appears in the surveillance networks.Existing methods usually assume the vehicle images are well cropped from the surveillance videos,and then use visual attributes,like colors and types,or license plate numbers to match the target vehicle in the image set.However,a complete vehicle search system should consider the problems of vehicle detection,representation,indexing,storage,matching,and so on.Besides,it is very difficult for attribute-based search to accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment.Moreover,the license plates may be mis-recognized in surveillance scenes due to the low resolution and noise.In this paper,a progressive vehicle search system,named as PVSS,is designed to solve the above problems.PVSS is constituted of three modules:the crawler,the indexer,and the searcher.The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images,metadata and contextual information to the server or cloud.Then multi-grained attributes,such as the visual features and license plate fingerprints,are extracted and indexed by the vehicle indexer.At last,a query triplet with an input vehicle image,the time range,and the spatial scope is taken as the input by the vehicle searcher.The target vehicle will be searched in the database by a progressive process.Extensive experiments on the public dataset from a real surveillance net work validate the effec tiveness of PVSS.展开更多
The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network ...The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.展开更多
文摘The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.
基金the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China(NSFC)under Grant No.61720106007the NSFC-Guangdong Joint Fund under Grant No.U1501254+2 种基金the National Key Research and Development Plan of China under Grant No.2016YFC0801005the NFSC under Grant No.61602049the 111 Project under Grant No.B18008.
文摘This paper is focused on the task of searching for a specific vehicle that appears in the surveillance networks.Existing methods usually assume the vehicle images are well cropped from the surveillance videos,and then use visual attributes,like colors and types,or license plate numbers to match the target vehicle in the image set.However,a complete vehicle search system should consider the problems of vehicle detection,representation,indexing,storage,matching,and so on.Besides,it is very difficult for attribute-based search to accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment.Moreover,the license plates may be mis-recognized in surveillance scenes due to the low resolution and noise.In this paper,a progressive vehicle search system,named as PVSS,is designed to solve the above problems.PVSS is constituted of three modules:the crawler,the indexer,and the searcher.The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images,metadata and contextual information to the server or cloud.Then multi-grained attributes,such as the visual features and license plate fingerprints,are extracted and indexed by the vehicle indexer.At last,a query triplet with an input vehicle image,the time range,and the spatial scope is taken as the input by the vehicle searcher.The target vehicle will be searched in the database by a progressive process.Extensive experiments on the public dataset from a real surveillance net work validate the effec tiveness of PVSS.
基金Project supported by the National Key Laboratory of CNS/ATMBeijing Key Laboratory for Network-Based Cooperative Air Traffic Managementthe National Natural Science Foundation of China(No.71731001)
文摘The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.
