Improved human action recognition approach based on two-stream convolutional neural network model

• Published in: The Visual computer Vol. 37; no. 6; pp. 1327 - 1341
• Main Authors: Liu, Congcong, Ying, Jie, Yang, Haima, Hu, Xing, Liu, Jin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Annotations; Artificial Intelligence; Artificial neural networks; Computer Graphics; Computer Science; Deep learning; Human activity recognition; Image Processing and Computer Vision; Kalman filters; Neural networks; Original Article; Performance evaluation; Recognition; Spacetime; Support vector machines; Training; Wavelet transforms; Human action recognition; Video surveillance; Motion history image; Faster R-CNN; Kalman filter

In order to improve the accuracy of human abnormal behavior recognition, a two-stream convolution neural network model was proposed. This model includes two main parts, VMHI and FRGB. Firstly, the motion history images are extracted and input into VGG-16 convolutional neural network for training. Th...