Human posture estimation method based on deformable convolutional neural network

A convolutional neural network and human posture technology, applied in the field of deep learning and computer vision, can solve problems such as low prediction accuracy, fusion of deep learning and attitude expression information, and estimation accuracy constraints, and achieve the effect of improving the recognition rate.

Active Publication Date: 2018-01-19
HARBIN UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This type of method has a fast execution speed, does not need to retain a sample library, has a small storage cost, and does not require a special initialization process. The disadvantage is that the estimation accuracy is restricted by the size of the training sample. Due to the complexity of human body pose estim

Method used

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  • Human posture estimation method based on deformable convolutional neural network
  • Human posture estimation method based on deformable convolutional neural network
  • Human posture estimation method based on deformable convolutional neural network

Examples

Experimental program
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Effect test

Embodiment 1

[0058] combine Figure 1-Figure 5 As shown, a method for estimating human body posture based on a deformable convolutional neural network disclosed in this embodiment includes the following steps:

[0059] Step a: Preprocessing the input data set, including expanding and segmenting the data set image;

[0060] Step b: Perform K-means clustering on the training set images;

[0061] Step c: Using the K-means clustering result as a label, input the training set into the deformable convolutional neural network for training;

[0062] Step d: Construct a scoring function F to train SVM for joint classification;

[0063] Step e: output processing;

[0064] The input data preprocessing includes:

[0065] a.1 read data set: in order to effectively train the network to improve the recognition rate of joint points, and to effectively illustrate the effectiveness of the present invention, the data set of the present invention has selected LSP (Leeds Sports Pose Dataset) data set and I...

Embodiment 2

[0068] Specifically, the step b includes performing K-means clustering on the training set pictures; the result of the clustering makes the adjacent joints in the same cluster, and at the same time uses the clustering result as a label, it is considered that there is a dependency between joints in the same cluster relationship, and input the clustering results into the convolutional neural network for training, so that the neural network has the function of identifying the dependencies between joints.

Embodiment 3

[0070] Specifically, the deformable convolutional neural network in the present invention includes a deformable convolutional layer, 3 traditional convolutional layers, 2 fully connected layers and an output layer; the training set fragments in the input neural network include two Information: one is the pixel position of the joint point, and the other is the dependency relationship between adjacent joints. The present invention obtains the apparent features of joint point image fragments through a deep convolutional neural network, and at the same time trains and recognizes dependencies between adjacent joint points. The kernel function adopted by the convolution layer in the neural network is a variable convolution kernel, which is specifically: the variable convolution layer adds an offset variable to the position of each sampling point in the convolution kernel; the present invention The partial convolutional layer of the convolutional neural network constructed in is diff...

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Abstract

The invention relates to the technical field of depth learning and computer vision, in particular to a human posture estimation method based on a deformable convolutional neural network. The method comprises the following steps of 1, preprocessing an input dataset, wherein expanding and data set picture cutting are included; 2, performing K-means clustering on training set pictures; 3, adopting the K-means clustering result as a label, and inputting a training set into the deformable convolutional neural network to be trained; 4, constructing a score function F for training an SVM for joint classification; 5, performing outputting treatment. By reading the positive data sets, some data sets are adopted as the training set, some data sets are adopted as the validation set, negative data isread for comparison, and the negative data set is introduced for training, so that the background can be effectively filtered away to obtain interference, and the recognition rate is increased.

Description

technical field [0001] The invention relates to the technical fields of deep learning and computer vision, in particular to a method for estimating human body posture based on a deformable convolutional neural network. Background technique [0002] With the development of various online games and the popularity of animated videos, it has become a very hot topic to correctly and quickly recognize and understand the gestures of people in images; this problem is collectively called gesture detection. Pose detection contains many categories and sub-problems, and pose estimation is one of them; pose estimation is one of the most important computer vision challenges nowadays because it can be quickly applied to person tracking, action recognition, and video-related tasks. In video analysis, such as video surveillance and video search, etc.; the practical application is very wide. [0003] In the field of computer vision, the main task of pose estimation is, given a picture, there...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
Inventor 宋立新卞龙鹏
Owner HARBIN UNIV OF SCI & TECH
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