Objective

2

Gait recognition has many advantages over DNA

fingerprint

iris

and 2D and 3D face recognition methods. For example

the observer does not need to cooperate in this method. In addition

the method can be performed at a relatively long distance and at a relatively lower image quality. Moreover

a person's gait is difficult to camouflage and hide. Therefore

gait recognition has become a research hotspot in recent years

and it is widely used in security

anti-terrorism

and medical applications

such as personal identification

treatment

and rehabilitation of abnormal leg and foot diseases. This paper proposes a novel gait human semantic segmentation method based on RPGNet (Region of Interest + Parts of Body Semantics + GaitNet) network to solve the problems of contour loss

human shadow

and long computing time caused by lighting

camera angles

and obstructions when gait recognition is performed by using a surveillance video in the field of anti-terrorism and security.

Method

2

This method is divided into the R (region of interest)

P (parts of body semantics)

and GNet (GaitNet) modules according to function. The R module obtains the area of interest of the gait body

which could improve computing efficiency and reduce image noise. First

the original image is processed by using the background subtraction method and translated into a binary image. Then

the image is operated by morphological processing methods

such as expansion

corrosion

and filtering. Second

we search the connected region of the human body in the graph and frame that area with a rectangular frame. Finally

we enlarge the length and width of the rectangular frame by a quarter and clip the image. Therefore

we obtain the connected regions of interest. The main function of the P module is to annotate gait body parts semantically by using LabelMe

an open-source image annotation tool. We train the human body according to its position. The semantics of the human body is defined as six parts:head

trunk

upper arm

lower arm

thigh

and lower leg. We map the semantics of the human body parts to six RGB information one by one. Then

we use LabelMe to annotate the image semantics captured by the camera

which generates the structure file of the image semantics annotation in XML format. Finally

the XML file and the original RGB image are imported into MATLAB to generate a human body part semantic annotation map. The GNet module designs a detailed semantic segmentation network model of the gait body. In the light of existing ResNet and RefineNet network models

we use ResNet model for reference to extract the high-level and the low-level semantics of the gait human body. The RefineNet network model is used to integrate low-level semantics with high-level semantics. Multi-resolution images generate fine low-level semantic feature maps and rough high-level semantic feature maps through residual network convolution units. Then

the feature maps are input into the fusion unit of multi-resolution feature map to generate the fused feature maps. Afterwards

the chained residual pools the fused feature maps to generate the fused pooled feature maps. Furthermore

the pooled feature maps of multi-resolution fusion are processed by output convolution. Thus

we obtain the semantically segmented feature maps. Finally

we use the softmax classifier to output the final gait semantics segmentation image by using bilinear interpolation. Through many experiments

we find that when the resolution is 1/8

1/16

1/32

and 1/64 of the original image

the semantics segmentation effect of the gait human body is better than that in other situations.

Result

2

A test conducted on 1 380 images from the gait database shows that the proposed RPGNet method has a higher segmentation accuracy in local and global information processing compared with six human contour segmentation methods

especially at viewing angles of 0°

45°

and 90°. In this study

we define the formula of segmentation accuracy

$$ρ$$

and experience shows that the accuracy of human gait segmentation is positively correlated with the rate of gait recognition. After a series of experiments

the RPGNet image semantics segmentation algorithm under the segmentation accuracy

$$ρ$$

whether at viewing angles of 0°

45°

or 90°

shows a high segmentation accuracy. Experiments on human segmentation under multi-person

hat-wearing

and occlusion conditions show that the RPGNet-based segmentation algorithm has a good grasp of global and local segmentation

high segmentation precision

and high contour integrity. The RPGNet algorithm can process eight frames of pictures per second

which could meet the real-time performance requirements of gait recognition.

Conclusion

2

The proposed gait semantic segmentation method can not only solve the problem of missing contours and human shadow caused by multi-covariates in outdoor conditions but also deal with the problem of contour difficult segmentation in conditions of outdoor multi-person segmentation

hat wearing

and occlusion. The use of the RPGNet-based human semantic segmentation method can improve the recognition rate of a gait recognition system

as indicated by an experiment on the relationship between recognition rate and segmentation accuracy. Simulations and analyses prove that the proposed RPGNet method shows improved human segmentation effect and high gait recognition rate in the conditions of multi-person scenes

people wearing a hat

and shielding. The training model of the image semantics segmentation algorithm is based on the deep learning model and the use of GPU to accelerate training. The training cost is higher than that of traditional machine learning methods

and the segmentation process is slower than that of traditional machine learning methods such as background subtraction. Further work will minimize the depth and complexity of the network model to improve the speed of training and testing.