Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different fr...Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different from the key actions.These noises pose a great challenge to human motion recognition.To solve this problem,we propose a new method based on the 3-Dimensional(3D)Bag of Visual Words(BoVW)framework.Our method includes two parts:The first part is the video action feature extractor,which can identify key actions by analyzing action features.In the video action encoder,by analyzing the action characteristics of a given video,we use the deep 3D CNN pre-trained model to obtain expressive coding information.A classifier with subnetwork nodes is used for the final classification.The extensive experiments demonstrate that our method leads to an impressive effect on complex video analysis.Our approach achieves state-of-the-art performance on the datasets of UCF101(85.3%)and HMDB51(54.5%).展开更多
In this paper, we consider the scalable of wireless sensor networks with trust-based security. In our setting, the nodes have limited capability so that heavy computations are not suitable. So public key cryptographic...In this paper, we consider the scalable of wireless sensor networks with trust-based security. In our setting, the nodes have limited capability so that heavy computations are not suitable. So public key cryptographic algorithms are not allowed. We focus on the scalability of the network and proposed new testing algorithms and evaluation algorithms to test new nodes added, which give them reasonable values of trust. Based on these algorithms, we proposed new components for trust management system of wireless sensor networks.展开更多
文摘Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different from the key actions.These noises pose a great challenge to human motion recognition.To solve this problem,we propose a new method based on the 3-Dimensional(3D)Bag of Visual Words(BoVW)framework.Our method includes two parts:The first part is the video action feature extractor,which can identify key actions by analyzing action features.In the video action encoder,by analyzing the action characteristics of a given video,we use the deep 3D CNN pre-trained model to obtain expressive coding information.A classifier with subnetwork nodes is used for the final classification.The extensive experiments demonstrate that our method leads to an impressive effect on complex video analysis.Our approach achieves state-of-the-art performance on the datasets of UCF101(85.3%)and HMDB51(54.5%).
文摘In this paper, we consider the scalable of wireless sensor networks with trust-based security. In our setting, the nodes have limited capability so that heavy computations are not suitable. So public key cryptographic algorithms are not allowed. We focus on the scalability of the network and proposed new testing algorithms and evaluation algorithms to test new nodes added, which give them reasonable values of trust. Based on these algorithms, we proposed new components for trust management system of wireless sensor networks.
