Gait Recognition Method Based on Attention 3D Frequency Convolutional Neural Network

Abstract

The present invention proposes a method for pedestrian recognition on gait images, using the attention mechanism to strengthen the learning of important features, and at the same time introducing a frequency convolution mechanism to give high-resolution dimensions to high-frequency feature information and low-resolution to low-frequency feature information rate size. On the one hand, it can greatly reduce the amount of calculated data. On the other hand, the high-resolution high-frequency features have better expressive ability, and the gait representation is more accurate. Further, the combination of 3D-CNN to extract spatio-temporal information is further improved model effect. Since the different angles of gait have a huge impact on the accuracy of model prediction, the present invention first trains the angle recognition network, and then trains the corresponding gait recognition network for each angle. In the final test, the angle recognition network is used to predict the Gait angle, and then select the corresponding gait recognition network for gait recognition. The experimental results prove that the method can effectively identify the gait of pedestrians.

Description

technical field [0001] The present invention relates to the field of biological feature recognition based on deep learning, in particular to a method for video pedestrian gait recognition. Background technique [0002] As a key feature in the field of machine vision and biometric recognition, gait features have opened up a new field of long-distance target recognition. Its main advantages are: the uniqueness of gait features, easy to perceive, difficult to camouflage and non-invasive. Based on the above advantages, gait recognition has a wide application prospect in video surveillance. [0003] The video image is a common way of displaying pedestrian gait in life scenes, which reflects the walking characteristics of pedestrians, but it is not likely that the image has defects such as light changes and noise disturbances, so it is very difficult to extract the features of the image. At the same time, pedestrian images have different angles, different clothes, and different d...

AI Technical Summary

Problems solved by technology

This method can learn the information of the entire video frame, and the information extraction is more comprehensive. At the same time, it can learn the space-time dimension information, and the gait description for different environments is more accurate, but the model has a large amount of calculation and complex parameter tuning.

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