Human gait recognition algorithm based on MobileNetV1 with attention mechanism

For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image; integrates the attention mechanism with MobileNetV1. The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIA-B gait database of the Chinese Academy of Sciences, and the experimental results are significantly improved with other deep learning models.