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Hybrid deep regression network-based head pose estimation method

A head posture and network technology, applied in the field of computer vision and machine learning, can solve problems such as inability to estimate head posture

Active Publication Date: 2018-07-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Classifiers commonly used in this type of method include Support Vector Machine (Support VectorMachine, SVM), Linear Discriminative Analysis (LDA), Kernel Linear Discriminative Analysis (KLDA), the main disadvantage of this type of method is It is impossible to estimate the output continuous head pose, see the literature: J.Huang, X.Shao, and H.Wechsler, Face Pose Discrimination using Support Vector Machines (SVM), International Conference on Pattern Recognition, pp.154-156, 1998

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  • Hybrid deep regression network-based head pose estimation method
  • Hybrid deep regression network-based head pose estimation method
  • Hybrid deep regression network-based head pose estimation method

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Embodiment Construction

[0080] Step 1: Preprocess the dataset;

[0081] For the BIWI (https: / / data.vision.ee.ethz.ch / cvl / gfanelli / head_pose / head_forest.html#) head pose dataset, read the corresponding head pose RGB image and Depth images, and save them separately; then according to the position of the camera when each image is collected, read the corresponding head pitch, yaw, and rotation angles of the N images, that is, the head attitude vector t n ∈ R 3 , t n The first dimension represents the pitch angle, the second dimension represents the tilt angle, the third dimension represents the rotation angle, and the subscript n represents the posture corresponding to the nth image; the RGB image is converted to a grayscale image for the collected RGB image. If the image is already a grayscale image, it does not need to be converted;

[0082] Step 2: Perform feature extraction on the dataset;

[0083] Normalize the head region image of the input image to an image with a size of 64×64 pixels, and ext...

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Abstract

The invention discloses a hybrid deep regression network-based head pose estimation method and belongs to the computer vision and machine learning field. According the method of the invention, on thebasis of a traditional regression algorithm and a deep network framework, image information of different modalities is applied to a head pose estimation problem for the first time; and sub-networks ofa plurality of modalities, which are obtained through training, are fused, so that a final hybrid deep regression network can be obtained. Compared with other head pose estimation methods, the algorithm of the invention can achieve higher estimation accuracy and have high robustness; and in addition, the method of the present invention has a certain universality, and is not only applicable to typical head pose estimation problems such as human-computer interaction, safe driving, and face recognition, but also can expand problem conditions to other deep regression problems.

Description

technical field [0001] The invention belongs to the field of computer vision and machine learning, and relates to the problem of head pose estimation in visual mapping. Background technique [0002] Head pose estimation refers to accurately and quickly estimating the deflection angle of the corresponding head in the image based on the digital image containing the head using machine learning and computer vision methods, also known as head pose. It is a hot issue in the field of computer vision and machine learning in recent years, and has a wide range of applications in human-computer interaction, safe driving and face recognition. For example: in the field of human-computer interaction, the deflection angle of the head can be used to control the direction and position of the computer or machine display; in the field of safe driving, the head posture can be used to assist the line of sight estimation, thereby prompting the driver to the correct line of sight direction; In th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46
CPCG06V40/20G06V10/507
Inventor 黄仰光潘力立
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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