Search

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Writing / Xiezuo is the property of Writing Magazine Agency, Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems that the existing human motion recognition methods based on deep learning have large parameters and the networks were too deep and heavy, this paper proposed a lightweight two-steam fusion deep neural network model and applied this model to human action recognition. This model combined a shallow multi-scale network with a deep network, and achieved a significant reduction in the amount of model parameters and avoided the problem that network was too deep. Experiments were performed on datasets UCFlOl and HMDB51, achieving 94.0% and 69.4% recognition accuracy in ImageNet pre-training mode, respectively. Experiments show that compared with the existing human motion recognition models based on deep learning, this model greatly reduces the parameter quantity and still has high motion recognition accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing human motion recognition method needed to input a fixed length video segment and underutilized the spatio-temporal information, this paper proposed a deep neural network model based on the combination of space-time pyramid and attention mechanism. This improved architecture combined 3D-CNN including spatio-temporal pyramids with LSTM model with spatio-temporal attention mechanism, and realized multi-scale processing of video segments and full utilization of complex spatio-temporal information of actions. For the architecture, the inputs of spatial and temporal domain were RGB image and the optical flow, the input of the fusion domain was the fusion feature of the motion and appearance features of the pyramid pooling layer. Finally, it used the decision fusion strategy to obtain the final motion recognition result. Experiments were performed on the UCFlOl and HMDB51 datasets, it achieved 94. 2% and 70. 5% recognition accuracy, respectively. The experimental results show that the improved network model achieves high recognition accuracy in video based human motion recognition tasks. [ABSTRACT FROM AUTHOR]

Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying batch normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93. 50%and 68. 32%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition. [ABSTRACT FROM AUTHOR]