Titlebook: Computer Vision and Image Processing; 4th International Co Neeta Nain,Santosh Kumar Vipparthi,Balasubramanian Conference proceedings 2020 S

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Managing Security and Workflowsnes, CNN models are trained in offline manner which involves re-training of a network considering seen as well as unseen data samples. However, such training takes too much time. This problem is addressed using proposed convolutional fuzzy min-max neural network (CFMNN) avoiding the re-training proc

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Maurizio Delmonte,Davide Moro,Andy McKaylligent video surveillance. Surveillance cameras are set up to monitor anomalous or unusual events. But, the majority of video data, related to normal or usual events, is accessible. Thus, analysis and recognition of anomalous events from huge data are very difficult. In this work, an automated syst

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Maurizio Delmonte,Davide Moro,Andy McKayuality and buffering continue to remain major concerns causing users to abandon streaming video. This is due to the conditional rule-based logic used by state-of-the-art algorithms, which cannot adapt to all the network conditions. In this paper, a Deep Neural Network (DNN) based adaptive streaming

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Maurizio Delmonte,Davide Moro,Andy McKays the deception through non-verbal behavior that can be recorded in a non-intrusive manner, the deception detection from video using automatic techniques can be devised. In this paper, we present a novel technique for the video-based deception technique using Deep Recurrent Convolutional Neural Netw

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