Abstract: Abstract： Camera video surveillance is widely used in debris flow disaster prevention and mitigation， but the existing video detection 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 Kinetics 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 surveillance in debris flow monitoring and warning.

Key words: Key words： debris flow, video surveillance, motion scene, transfer learning, intelligent recognition