Research On Continuous Action Recognition Based On Deep Learning

It is of great significance to realize the continuous action recognition of human body through intelligent video surveillance in the construction site to ensure the safety of workers.Continuous action is composed of multiple actions,each action duration is uncertain,has a certain complexity,but some deep learning network structure complexity,low accuracy,for human continuous action recognition there are some defects.hence,in this paper,continuous action recognition is studied,from the point of view of single action,a G-ResNet network model with attention mechanism is designed,and then continuous action recognition is completed with sliding window.Aiming at the problem that the existing model can not better extract the temporal and spatial features of video,this paper proposes a human action recognition model based on G-ResNet network.Firstly,the model uses ResNet34 network to extract deep spatial features to solve the problem of deep network degradation.Secondly,the GRU network is used to obtain the timing information between video frames and to process the long-term dependence between frame sequences.Finally,the three-step training strategy is used to optimize the network model and improve the accuracy of action recognition.To solve the problem of insufficient feature information extraction in G-ResNet network,this paper proposes a human action recognition model based on FSAG-ResNet network.The model is based on the G-ResNet network.Firstly,the spatial pyramid pooling operation is introduced into the ResNet34 network,and the features are extracted with multi-scale windows to enrich the extracted features.Secondly,the time-attention mechanism is integrated the GRU network.According to the importance of video frame sequence,different weight values are assigned to the GRU network to capture more key features,the accuracy of action recognition is further improved.A sliding window combined with FSAG-ResNet network method is proposed to realize continuous action recognition in infrastructure construction site.First,the single action and continuous action video data set of different scenes in the construction site is established,and then the FSAG-ResNet network is applied to the construction site by using the idea of transfer learning.Finally,the continuous action video is recognized by smoothing window to complete the continuous action recognition.The experimental results show that the accuracy of the FSAG-ResNet network model in UCF101 is 96.2 and 64.3 in HMDB51,which is greatly improved compared with other mainstream networks.At the same time,the sliding window combined with the FSAG-ResNet network model is used for continuous action recognition.Each action in the continuous action video can be detected in real time,and the average recognition rate is 88.79,which verifies the effectiveness of the algorithm in this paper.