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Human pose estimation based on deformation convolution

A technology of human body posture and convolution, applied in the fields of computer vision and pattern recognition, can solve the problems of unrobust estimation performance, immature, and no optimization of estimation results, etc., and achieve simple biased convolution graphs, increased accuracy, and easy operation simple effect

Active Publication Date: 2019-02-22
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

This method deals with video images, and has the following shortcomings: the network used does not have a strategy to optimize the estimation results; the estimation method does not consider multi-scale features, which will affect the accuracy
[0006] To sum up, the problem of the existing technology is that, for natural color images, in complex scenes, the human body poses special or the limbs are distorted, due to the influence of light refraction or reflection due to environmental reasons, the scale of the human body in the image changes When it is larger, the estimation is not accurate enough, the estimation performance is not robust, immature, and cannot reach the application level

Method used

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  • Human pose estimation based on deformation convolution
  • Human pose estimation based on deformation convolution
  • Human pose estimation based on deformation convolution

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] In complex scenes, the posture of the human body is special or the limbs are distorted. Due to the influence of light refraction or reflection due to environmental reasons, when the scale of the human body in the image changes greatly, the estimation is not accurate enough, and the estimation performance is not robust and immature. Unable to reach app level. The present invention conducts research in view of these current situations, and proposes a human body pose estimation method based on deformed convolution, see figure 1 , including the following steps:

[0046] (1) Get training images:

[0047] (1a) Use the target detection network Mask RCNN to detect images containing people, detect people targets, separate the individuals, and return the bounding boxes of the individual images.

[0048] (1b) Crop the bounding box to obtain the individual image of the person, fill it with constant values ​​around the image to make it a square image, use it as a training image an...

Embodiment 2

[0065] The human body pose estimation method based on deformed convolution is the same as that of Embodiment 1. The deformed convolution module of the deformed convolution kernel described in step 3, and its forward propagation steps are as follows:

[0066] 3.1. Input the input feature map of the deformed convolution module of the deformed convolution kernel into the bias convolution, and obtain the convolution kernel sampling bias feature map output by the bias convolution. The size of the convolution kernel sampling bias feature map should be set is H×W, where H and W are the height and width of the output feature map, respectively, and the number of channels for the bias feature map should be set to 2 k 2 ·n c , where k is the edge length of the convolution kernel, n c is the number of input channels, the bias feature map contains the bias Δp for the two axes corresponding to the sample points in each convolution kernel on the feature map in each channel of the input n ....

Embodiment 3

[0077] The human body pose estimation method based on deformed convolution is the same as that of Embodiment 1-2. The deformed convolution module of the deformed feature map described in step 3, and its forward propagation steps are as follows:

[0078] 3.3. Input the input feature map of the deformed convolution module of the deformed feature map into the bias convolution, and obtain the bias feature map of the input feature map output by the bias convolution. The size of the bias feature map of the input feature map should be set to is H×W, where H and W are the height and width of the input feature map, respectively, and the number of channels in the bias feature map should be set to 2n c , n c is the number of input channels, the bias feature map contains the bias Δp for the two axes of each point on the input feature map in each channel 0 ;

[0079] 3.4, the bias Δp in the bias feature map according to the input feature map 0 Get the deformed convolution output y, in p...

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Abstract

The invention discloses a human posture estimation method based on deformation convolution, which solves the technical problem of estimating human posture from images. The method comprises the following steps: obtaining a training image; making a joint heat map; constructing a deformation convolution forward propagation module; constructing residual blocks and constructing multi-scale hourglass networks with deformed residual blocks; training and stacking of multi-scale hourglass networks with deformed residual block network structures; acquring that result of the human posture estimation. Theinvention uses deformation convolution and improves the internal connection mode of the hourglass network, a stacked multi-scale hourglass network with deformed residual blocks is constructed, For asingle natural color image, the human pose can be estimated more accurately by extracting and organizing the image features in the complex scene of distorted limbs or special posture, refraction or reflection of light, large variation of human scale, and occlusion. Used for human-computer interaction in multiple scenarios.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, and in particular relates to human body posture estimation, in particular to a human body posture estimation method based on deformation convolution. The invention is applied to precisely locating each joint point of a human body in a complex scene to accurately estimate the posture of the human body. Background technique [0002] As an important research direction in the field of computer vision and pattern recognition, as well as a key issue in human-computer interaction intelligence, human pose estimation is of great significance for computers to effectively understand and process human activities in image data, and is widely used in human activity analysis , intelligent monitoring, behavior tracking, human-computer interaction and other fields. Human body pose estimation refers to the process of positioning and labeling the joint points and parts of the human ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V40/20G06V10/44G06N3/045G06F18/24
Inventor 高新波窦睿翰路文孙晓鹏何立火郭兆骐
Owner XIDIAN UNIV
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