Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose...Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.展开更多
Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are mat...Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.展开更多
基金supported in part by National Basic Research Program of China (973 Program) under Grant No. 2011CB302203the National Natural Science Foundation of China under Grant No. 61273285
文摘Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.
基金supported by the National Key Basic Research Program of China (973 Program) under Grant No. 2011CB302605the National High Technical Research and Development Program of China (863 Program) underGrants No. 2010AA012504,No. 2011AA010705+1 种基金the National Natural Science Foundation of China under Grant No. 60903166the National Science and Technology Support Program under Grants No. 2012BAH37B00,No. 2012-BAH37B01
文摘Automatic signature generation approaches have been widely applied in recent traffic classification.However,they are not suitable for LightWeight Deep Packet Inspection(LW_DPI) since their generated signatures are matched through a search of the entire application data.On the basis of LW_DPI schemes,we present two Hierarchical Clustering(HC) algorithms:HC_TCP and HC_UDP,which can generate byte signatures from TCP and UDP packet payloads respectively.In particular,HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads.Further,in order to deal with the case in which byte signatures cannot be derived,we develop an algorithm for generating bit signatures.Compared with the LASER algorithm and Suffix Tree(ST)-based algorithm,the proposed algorithms are better in terms of both classification accuracy and speed.Moreover,the experimental results indicate that,as long as the application-protocol header exists,it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.
