Intelligent Identification Method of Debris Flow Scene Based on Camera Video Surveillance
Camera video surveillance is widely used in debris flow disaster prevention and mitigation,but the existing video de-tection technology has limited functions and can not automatically judge the occurrence of debris flow disaster events.To solve this problem,using transfer learning strategy,this paper improves a video classification method based on convolutional neural network.Firstly,with the help of TSN model framework,the underlying network architecture is changed to ResNet-50,which is utilized for motion feature extraction and debris flow scene identification.Then,the model is pre-trained with ImageNet and Ki-netics 400 datasets to make the model have strong generalization ability.Finally,the model is trained and fine-tuned with the pre-processed geological disaster video dataset,so that it can accurately identify debris flow events.The model is tested by a large number of moving scene videos,and the experimental results show that the identification accuracy of the method for debris flow movement video can reach 87.73%.Therefore,the research results of this paper can to the play a full role of video surveil-lance in debris flow monitoring and warning.
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