LAUSR

Abstract. The purpose of behavior recognition is to recognize the actions of the human body in action. It plays a great role in surveillance, video recommendation, and human-computer interaction with video. With the rise of neural networks, behavior recognition has also continued to develop and progress and has reached a relatively advanced level. However, behavior recognition is still insufficient in recognizing complex human movements and recognizing videos in different bands. To solve this problem, this paper establishes a convolutional neural network (CNN) cross-spectral human behavior recognition algorithm based on global time domain representation. It adopts the method of time-domain feature extraction, construction of optimized convolutional neural network layers, and global time-domain cross-spectrum construction. It also uses videos from the unified compliance framework (UCF)-sports and UCF-11 datasets for experiments. Experiments show that the algorithm achieves an average accuracy of 90% in the behavior recognition of UCF-sports. It still maintains an average accuracy rate of >90  %   in the more complex behavior recognition of UCF-11, and the highest accuracy rate is 93%.