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
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[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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