Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of ...Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.展开更多
In multi-agent systems(MAS),finding agents which are able to service properly in an open and dynamic environment are the key issue in problem solving.However,it is difficult to find agent resources quickly and positio...In multi-agent systems(MAS),finding agents which are able to service properly in an open and dynamic environment are the key issue in problem solving.However,it is difficult to find agent resources quickly and position agents accurately and complete the system integration by the keyword matching method,due to the lack of clear semantic information of the classical agent model.An semantic-based agent dynamic positioning mechanism was proposed to assist in the system dynamic integration.According to the semantic agent model and the description method,a two-stage process including the domain positioning stage and the service semantic matching positioning stage,was discussed.With this mechanism,proper agents that provide appropriate service to assign sub-tasks for task completion can be found quickly and accurately.Finally,the effectiveness of the positioning mechanism was validated through the in-depth performance analysis in the application of simulation experiments to the system dynamic integration.展开更多
文摘Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.
基金Projects(61173026,61373045,61202039)supported by the National Natural Science Foundation of ChinaProject(2012AA02A603)supported by the National High Technology Research and Development Program of China+1 种基金Projects(K5051223008,K5051223002)supported by the Fundamental Research Funds for the Central Universities of ChinaProject(513***103E)supported by the Pre-Research Project of the"Twelfth Five-Year-Plan"of China
文摘In multi-agent systems(MAS),finding agents which are able to service properly in an open and dynamic environment are the key issue in problem solving.However,it is difficult to find agent resources quickly and position agents accurately and complete the system integration by the keyword matching method,due to the lack of clear semantic information of the classical agent model.An semantic-based agent dynamic positioning mechanism was proposed to assist in the system dynamic integration.According to the semantic agent model and the description method,a two-stage process including the domain positioning stage and the service semantic matching positioning stage,was discussed.With this mechanism,proper agents that provide appropriate service to assign sub-tasks for task completion can be found quickly and accurately.Finally,the effectiveness of the positioning mechanism was validated through the in-depth performance analysis in the application of simulation experiments to the system dynamic integration.
