Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodie...Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.展开更多
A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called corre...A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally, a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.NRF-2022R1I1A1A01069526).
文摘Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and sports.Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions.There are various related studies on the real-time classification of actions in an image.However,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time analysis.In addition,it has low accuracy of action of each object ifmultiple objects appear in the image.Also,it needs to be improved since it has a memory overhead in processing image data.Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology.The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image.Then,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector.Compared to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per frame.The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network.Therefore,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.
文摘A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally, a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods.
