Infrared behavior recognition based on spatio-temporal two-stream convolutional neural networks

Aiming at the recognition of human behavior in infrared video, an infrared human behavior recognition method based on spatio-temporal two-flow convolutional neural network was proposed. In this method, first the entire infrared video is equally segmented, and then the infrared image extracted randomly and the corresponding optical flow image in each video segment are input into the spatial convolutional neural network, and the spatial network can effectively learn which part of the infrared image is actually the action by merging the optical flow information. Next the recognition results of each small segment are merged to get the spatial network results. At the same time, the randomly selected optical stream image sequence in each segment of the video is input into the temporal convolutional neural network, and the result of the temporal network can be obtained by fusing the result of each small segment. Finally, the results of spatial network and the temporal network are weighted and summed to obtain the final video classification results.In the experiment, the action on the infrared video data set containing 23 kinds of infrared behavior action categories was identified by this method, and the correct recognition rate was 92.0%. The results show that the algorithm can effectively identify the infrared video behavior.