Head posture estimation method, system and equipment based on multi-scale lightweight network and medium

Abstract

The invention provides a head attitude estimation method, system and device based on a multi-scale lightweight network, and a medium. The method comprises the following steps: obtaining a data set containing a head attitude, and preprocessing the data set; extracting the preprocessed data set by using a multi-scale convolutional network to obtain a corresponding feature map; training a lightweight network based on the feature map to obtain a MobileNet regression device model; and obtaining a head image of an image to be detected, and inputting the head image into the MobileNet regression device model for head posture prediction to obtain head posture information of the image to be detected. According to the method, the feature map in the data set is extracted by adopting the multi-scale convolution kernel, and the convolution kernels of different scales are used for extracting features of the input head posture image, so that the image features are enriched, the image information is reserved, and the accuracy of head posture estimation is improved; and meanwhile, the MobileNet regression device model is trained based on the lightweight network, and the calculation amount is greatly reduced on the premise that the network performance is not lost.

Description

technical field

[0001] This application belongs to the field of machine learning and computer vision, and in particular relates to a head pose estimation method, system, device and medium based on a multi-scale lightweight network. Background technique

[0002] Head pose estimation is generally defined in computer vision as the use of machine learning methods to estimate the relative deflection angle between the head and the camera in the image based on a digital image containing the head. Usually, the head pose of a person has three degrees of freedom. The directions are the yaw angle in the horizontal direction, the pitch angle in the vertical direction, and the rotation angle in the image plane, respectively. In the context of the needs of authentication, safe driving, and human-computer interaction, head pose estimation, as a key problem in these practical applications, has received increasing attention in the fields of computer vision and machine learning in recent year...

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