AttentionNet: An Efficient Scheme for Human Activity Recognition

Aiming at the problem of noise interference in two-dimensional radar signals, an efficient human behavior recognition scheme based on attention mechanism and convolutional neural network (CNN) is proposed. This algorithm combines attention mechanism and spatial pyramid pooling (SPP) layer with CNN, and fuses hierarchical feature maps generated by the network to reduce noise interference. By comparing the performance of proposed method with that of common CNNs, the experimental results show that the proposed algorithm acts more effectively under different noises. Especially, when the signal-to-noise ratio (SNR) is higher than -10dB, an accuracy rate of more than 90% could be achieved.