Objective

2

The rapid development of modern network and computer technology has led people to gradually move toward the information and intelligent era. In human pose estimation

advanced semantic interpretation and judgment results are obtained through processing

analyzing

and comprehending the input image or image sequence with computer. Human pose estimation has a wide range of applications and development prospects in human-computer interaction

surveillance

image retrieval

motion analysis

virtual reality

perception interface

etc. Thus

human pose estimation based on image is an extremely important research topic in the field of computer vision. However

the problem of human pose estimation has always been a difficult and hot topic because of the influence of the diversity of human visual appearance

occlusion

and complex background. In this paper we consider the problem of human pose estimation from a single still image. Traditional 2D human pose estimation algorithms are based on the pictorial strictures (PS) models. Solving the problem with the following is difficult:human pose estimation algorithms based on the PS model need to detect human parts in images

but in real world

detecting a single member of the human body is very difficult because of the background noise and the wide variety of human appearance. In recent years

the development of deep learning has led to new methods for human pose estimation. Compared with traditional algorithms

deep models have deeper hierarchies and ability to learn more complex patterns. In this work

we mainly focus on the effect of initial features on human joint point positioning and propose cross-stage convolutional pose machines (CSCPM).

Method

2

First

the VGG network is used to obtain the preliminary initial features of the image

which is the basis of the image joint point positioning. The VGG network inherits the frameworks of LeNet and AlexNet and adopts a 19-layer deep network. The VGG network is the preferred algorithm to extract convolutional neural network (CNN) features from the images. The initial features retain more original information because the VGG network directly processes the image. Learning parameters in the deep convolutional network is difficult due to the interference of self-occlusion and mixed background. Second

on the basis of initial features

a multistage model is constructed to study the structural features at different scales. The multistage model consists of a sequence of convolutional networks that repeatedly produce 2D belief maps for the location of each part. The initial features are concatenated in each subsequent stage feature to solve the problem of gradient disappearance in initial feature learning. The network is divided into six stages. The first and second stages use the original image as input

and the third to sixth stages use the feature maps produced by the second stage as input. Finally

the joint loss function of the multi-scale joint location is designed to learn parameters in the deep convolutional network. Each stage of the cross-stage convolutional pose machines (CSCPM) effectively enforces supervision in intermediate stages through the network. Intermediate supervision has the advantage that even though the full deep learning architecture can have many layers

it does not fall prey to the vanishing gradient problem as the intermediate loss functions replenish the gradients at each stage. We encourage the network to repeatedly arrive at such representation by defining a loss function at the output of each stage that minimizes the Euclidean distance between the predicted and ideal belief maps for each part.

Result

2

We evaluate the proposed method on two widely used benchmarks

namely

MPⅡ (MPⅡ human pose dataset) and extended LSP (leeds sport pose) dataset

and compare the method with other human pose estimation methods in the past three years in terms of qualitative and quantitative analyses. In the experiments

percentage of corrected keypoints (PCK) measure is used to evaluate the performance of human pose estimation methods

where a key-point location is considered correct if its distance to the ground truth location is no more than a certain threshold for the length of a portion of the body. The official benchmark on the MPⅡ dataset adopts PCKh (using portion of head length as reference) at 0.5

while the official benchmark on the LSP dataset adopts PCK at 0.2. In the MPⅡ dataset

the total detection rate of the model is 89.1%

which 0.7% points higher than that of the model with the second highest performance. In the LSP dataset

the total detection rate of the model is 91.0%

which is 0.5% points higher than that of the model with the second highest performance. The qualitative results fully show the benefits of the cross-stage structure. The detection results are improved in some scenes

such as occlusion and complex background

because the concatenated initial features retain the original information.

Conclusion

2

The human pose estimation model CSCPM is designed aiming at the failure cases of the convolutional pose machines (CPM) in some complex

scenes such as self-occlusion

mixed background

and joints of nearby people. The model provides a sequential prediction framework for the task of human pose estimation

which introduces a cross-stage structure based on the CPM model. The experimental results show that the proposed model improves the accuracy of human pose estimation and further accurately locates the points of the joins. The effectiveness of the proposed initial features learning and the benefit in the cross-stage structure are evaluated on two widely used human pose estimation benchmarks. Our approach achieves state-of-the-art performance on both datasets. The initial feature learning can effectively judge the self-occlusion and mixed background interference of the joints. The CSCPM

a human pose estimation model with cross-stage structure

is superior to existing human pose estimation models.