In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombina...In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.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
基金Supported by the National Natural Science Foundation of China (70071042,60073043,60133010)
文摘In this paper, a new algorithm for solving multi-modal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.
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.
