Two stream skeleton behavior recognition algorithm based on Motif-GCN①

WU Jin(吳 進(jìn)),WANG Lei,FENG Haoran,CHONG Gege

(School of Electronic Engineering,Xi′an University of Posts and Telecommunications,Xi′an 710121, P.R.China)

Abstract

Key words: skeleton behavior recognition,Motif-GCN,two stream network

0 Introduction

Human behavior recognition is of great research significance in computer vision.In essence,it enables computers to recognize human behavior intelligently.Based on this characteristic,it is widely used in video surveillance[1], intelligent transportation[2], humancomputer interaction,virtual reality simulation[3],intelligent security[4]and smart home,etc.

Compared with the methods based on RGB(red green blue) images or videos,the behavior recognition based on human skeletons data is less susceptible to the influence of external factors and has better robustness.Therefore,it has been widely studied.Traditional deep learning-based skeletons behavior recognition methods convert human skeletons data into vector sequences or two-dimensional grids,and then input them into convolutional neural network (CNN) or recurrent neural network (RNN) for prediction.However,the graph structure of human skeletons data itself is neglected in this method,and the dependence relationship between related joints cannot be represented fully.However,the graph convolution network(GCN)emerged in recent years can well deal with the data with irregular topology structure.In addition,the continuous maturation and development of equipment capturing human skeletons coordinates in recent years,such as Openpose, Optical Camera, Microsoft Kinect, Intel RealSense.As a result,human skeletons behavior recognition based on GCN has been widely studied.In 2018,Yan et al.[5]proposed the application of GCN to skeletal behavior recognition.They regarded human joint nodes as graph nodes,human joints naturally connected and the same joints in continuous frames as edges to construct spatio temporal graph convolutional network(ST-GCN).However,ST-GCN has limitations in the process of graph construction.(1) The graph in STGCN only represents the physical structure of the human body,so it cannot better identify some human actions.For example,the action of putting on shoes,it is a very important human activity,but ST-GCN is difficult to capture the relationship between hands and feet.(2)ST-GCN only contains the first-order information of the human skeletons,namely the joint information,while the second-order information which also contains important action information of the skeletons is ignored.(3) The structure of GCN is hierarchical,and different layers contain semantic information of multiple layers.However,the graph topology in ST-GCN is fixed in all layers,which lacks the flexibility and ability to model the multi-level semantic information in all layers.Refs[6 -14] and others on the basis of these problems have made the improvement.Aiming at problem(1) and problem (2),the use of Motif-GCN is proposed in this paper to extract spatial features of skeleton graph.The relationship between adjacent joint nodes that are naturally connected by the human body are mainly considered in the first Motif,and the relationship between joints that are not physically connected are considered in the second Motif.

The temporal features is extracted by the temporal convolutional network (TCN),the length and direction of the vector between two joints are regarded as the length and direction of the bone,and it's added to the GCN to predict the action label like the first-order information.Finally,the results of joint flow and bone flow are combined to obtain the final result.

1 Related work

1.1 Skeleton-based behavior recognition

The traditional behavior recognition based on skeletons mostly depends on the manual design features[15],however,the traditional way is based on the prior knowledge of researchers or data feature extraction,to some extent,the action characteristics of human behavior is often reflected,and it could not fully represent the overall state, and is susceptible to outside influences.With the rapid development of deep learning,behavior recognition based on deep learning has become a research hotspot.Common deep learning techniques include 3D convolutional neural network (3DCNN) model[16],RNN model[17],two stream CNN model[18]and hybrid network model[19].CNN-based methods convert skeleton data into pseudo-images based on hand-designed transformation rules,representing temporal dynamics and skeleton joints in the form of rows and columns,while RNN-based methods convert skeleton data into coordinate vector sequences.3DCNN model is to stack some frames into a cube shape and uses 3D convolution kernel for feature extraction.The idea of two stream convolution network is to input RGB information and optical flow field information of video frames into a CNN grid respectively,and then make prediction respectively.Finally,the final result is obtained by fusing the two prediction results.Hybrid network models include the combination of CNN and RNN,and the combination of CNN and long short term memory (LSTM).Spatial features are extracted by the former,while the temporal features are extracted by the latter.However,a large amount of data and parameters are required by the 3D-CNN,which is not conducive to the extraction of long-term features.The two stream method can only extract the temporal features of the before and after frames.The extracted temporal features are not very comprehensive,and the hybrid network model is difficult to combine them, with too many parameters and high resource consumption,which is difficult to deploy in reality.Since the skeleton data is the structure of a graph,it cannot be converted into vector sequences or two-dimensional grids to extract features well.Compared with CNN and RNN in recent years,GCN can handle this data structure well.In 2018,Yan et al.[5]proposed the application of GCN in behavior recognition based on human skeletons data,and a variety of improved methods have emerged since then.Based on Ref.[20],Motif-GCN is adopted to extract spatial features and TCN is adopted to extract temporal features.At the same time,the bone information is added to construct a two stream structure to further realize the purpose of strengthening the relationship between the joints of the body.

1.2 Graph convolutional network

Ref.[21]combined deep learning technology with graph data for the first time and graph neural network(GNN) is proposed,which made the deep learning technology be effectively used in the related scenes of graph data.Due to the success of CNN,the concept of CNN is generalized from grid data to graph data,and thus GCN is generated.

Construction on the graph of GCN method usually can be divided into the method based on spectral domain[21]and the method based on spatial domain[22].GCN based on spectral domain is similar to the convolution theorem,the spatial domain of the signal through the Fourier transform to the spectral domain for multiplication operation,treatment after the transformation to the spatial domain,GCN method based on spectral domain is defined in the spatial domain of graph nodes signal through the graph spectral domain Fourier transform into,and then,finally a method to transform back to the spatial domain.The method based on spatial domain is to directly process the graph signal.In the CNN convolution operation,each pixel is treated as a node,and the new features of the node are obtained by calculating the weighted average value of the neighbor nodes around the node.As for graph data,its structure is irregular,so it can not directly realize feature extraction by sliding convolution kernel like CNN.In GCN,the neighboring nodes of each node are usually sampled,and then these neighbor nodes are divided into different subsets to realize weight sharing and finally realize feature extraction.

2 Introduction to algorithm Principle

In the traditional method of behavior recognition based on human skeletons,in the graph structure constructed with human joints as nodes and bone as edges,only adjacent nodes of joints are generally considered when using GCN to extract spatial features.In this paper,Motif-GCN is used to extract spatial relationship between joints.The relationship between joints that are directly adjacent is encoded by the first Motif-GCN,and the relationship between joints that are disconnected is encoded by the second Motif-GCN,so as to strengthen the relationship between physically connected and non-physically connected joints and capture higher-order information.In addition,bone information is also introduced to construct the two stream structure of bone and joints.

2.1 Motif-GCN

Compared with CNN,the convolution kernel has translation invariance,and graph structured data does not have this property because of its unique structure.Therefore,the biggest difference between GCN and CNN is the definition of sampling function and weight function.In the previous methods,most of them chose to sample the first-order or second-order neighbor nodes of each node,as shown in Fig.1,and divided them into root nodes,centripetal nodes,and centrifugal nodes according to their distance from the center of gravity and the distance from the root node to center of gravity.

As shown in Fig.1, it is the traditional neighbor node sampling rules,where 0 represents the root node,1 represents the centripetal node,2 represents the centrifugal node,and the cross represents the center of gravity.

Fig.1 Traditional neighbor node sampling rules

In the traditional graph convolution operation,the convolution operation at a node can be expressed as in Eq.(1)[5],wherevtirepresents the central node,vtjrepresents the neighbor node ofvti,Prepresents the sampling function,Wrepresents the weight function,andZis shown in Eq.(2),Zrepresents the number of subsets divided by neighbor nodes,lti(vtj) represents the label to whichvtjbelongs in the molecular set label withvtias the center node,which is used to balance the contribution of different subsets.

Although joints moved in groups when people perform movements,a single joint may appear in multiple parts of the body.In this sense,learnable maskMis added to each layer of spatio temporal graph convolution[5].The mask will scale the contribution of a node's features to its neighbors based on the learned importance weight of each spatial graph edge in the edges naturally connected by the human body.Therefore,the graph convolution operation is finally represented as

In Eq.(3),finrepresents the input,Akrepresents the adjacency matrix,Mkrepresents the learnable mask,andKvrepresents the kernel size of the spatial dimension,☉denotes the dot product.

In 2019,Wen et al.[20]proposed Motif-GCN.Motif refers to the connection pattern between different node types,and a double Motif structure was constructed in Motif-GCN.The first Motif-GCN is used to encode the relationship between joints directly connected in the skeleton structure of human body,the relationship between joints without connection in human skeleton structure is encoded by the second Motif-GCN.In the process of human movement,joints without connection often contain important action information.For example,in the movement of the right hand touching the left foot,although there is no connection between the hand and feet,the relationship between them is useful for identifying the movement of foot touching.Therefore,the addition of this Motif can achieve the capture of higher-order information.

The problem that the traditional graph convolution method for skeleton structure modeling only considers the physical connection neighbors of each joint and joints of the same type is solved by Motif-GCN,which cannot capture higher-order information.Different from Ref.[20],Motif-GCN is used for reference and Motif-GCN is choosed to extract spatial features while continuing to use TCN to extract temporal features.In addition,bone information was added in this paper to form a two stream structure with joint information.Then,the two were input into the 9-layers model composed of Motif-GCN and TCN respectively,and finally,Softmax classification function was used to obtain the classification results,and then a fusion of the two results was carried out to obtain the final classification verification results.The experimental results shown by the final combined results were significantly improved and enhanced compared with the previous classical models.

Two Motifs are used to simulate the physical connection and non physical connection of the human skeletons.In the first Motif,only the nodes with direct adjacent relationships are considered.The neighbor of each joint has three characters,the joint itself,the parent nodes of joint,and the child nodes of joint.It is shown in Fig.2.

Fig.2 Graph structure in the first Motif

As shown in Fig.2, the graph structure in the first Motif is shown,where each circle represents a joint point,the line with arrow represents the natural physical connection of the human body,and the arrow represents from the parent node to the child node.

In the second Motif,the joints naturally connected by the human body are not considered,but the joints without physical connectivity are mainly considered.The weighted adjacency matrix between the disconnected joints is defined by allocating a large weight to the joints with short distance.In the weighted adjacency matrix,the relationship between nodeiand nodejcan be expressed asαi,j=maxe-e(i,j),whereeis a matrix representing the average euclidean distance between pairs of nodes in the sequence.The relationship between the joint that is not connected to the neck joint and the neck joint is shown in Fig.3.The calculation formula of Euclidean distance between node 1 (x1,x2,…,xn) and node 2 (y1,y2,…,yn) inn-dimensional spaces is shown in Eq.(4).

As shown in Fig 3,It is the structure between nodes in the second Motif.The two joints without physical connection is connected by the dotted line.

Fig.3 Graph structure in the second Motif

Finally,Motif-GCN can be expressed as

In Eq.(5),Xt∈RN×Drepresents the input.There areNnodes andDcoordinates in framet,andKMrepresents the dependency between different semantics.Because the neighbor of each joint in the first Motif has three semantic roles,KM1=3,KM2=1;in the second Motif,represents the adjacency matrix corresponding to each Motif,andDis an angle matrix,whererepresents the weight matrix corresponding to the node typekin each Motif,is output.

2.2 Overall network structure

The process of behavior recognition using Motif-GCN is shown in Fig 4.After bone sequence input,spatial features are extracted by Motif-GCN and temporal features are extracted by TCN.Finally,the final classification results are obtained through Softmax classifier.The specific algorithm process is shown in Fig.4.

Fig.4 The process of behavior recognition by Motif-GCN

As shown in Fig.5,it is the overall flow chart of the Motif-GCN algorithm.Firstly,the skeleton sequence is input to obtain joint information and bone information,and then the two are fed into the Motif-GCN and TCN structures respectively.The Motif-GCN and TCN are followed by a batch normalization (BN) layer and ReLU layer.A Dropout layer is also added between the Motif-GCN and TCN.There are 9 layers in this structure,and the numbers below each layer represent the input channel number,output channel number and step size information of that layer.Then,the results are sent to the Softmax classifier to get the respective classification,Finally,the final classification results are obtained by result fusion.

Fig.5 Overall flow chart of two stream Motif-GCN algorithm

3 Experimental results and analysis

3.1 Dataset introduction

NTU-RGB+D[23]dataset consists of 56 880 action samples,including RGB video,3D skeleton data,depth map sequence and infrared video for each sample.There are 60 categories,which are mainly divided into 3 groups,40 daily activities,such as drinking,eating,reading;9 health-related movements,such as sneezing,rocking and falling,and 11 reciprocal movements,such as answering,kicking and hugging.This data set was captured simultaneously by three Microsoft Kinect V.2 cameras,with a resolution of 1920 ×1080 for RGB video,512 ×424 for both depth map and infrared video,and 3D skeleton data containing the 3D positions of 25 major body joints per frame.NTU-RGB + D dataset adopts two different partitioning criteria when dividing training set and test set: Cross-Subject (X-Sub) and Cross-View (X-View).

Cross-Subject (X-Sub): in the cross-subject,a total of 40 subjects are used to collect data,and the age of these subjects is between 10—35 years old.This data set divides these 40 subjects into training group and testing group,with 20 subjects in each group,and the training set and testing set contain 40 320 and 16 560 samples respectively.

Cross-View(X-View): in the cross-view,three different horizontal views of - 45 °and + 45 ° were captured from the same action using three cameras at the same height.Each subject was required to make each action twice,once facing the left camera and once facing the right camera.Two front views,one left view,one right view,a left 45 ° degree view and a right 45 °view can be captured by this way.

Kinetics:data set contains about 300 000 video clips retrieved from YouTube.These videos cover up to 400 human action courses,from daily activities,sports scenes to complex interactive actions.In Kinetics,each clip lasts about 10.240 000 videos are used to train and 20 000 videos are used for verification.Train the comparison model on the training set and report the accuracy on the verification set.

3.2 Experimental platform

As shown in Table 1,this experiment is carried out in Linux system,based on CUDA platform and combined with PyTorch deep learning framework.PyTorch version is 1.1.0,and GPU version is GTX 1080 Ti.The memory is Kingston HyperX Savage DDR4 and the hard drive is Seagate in 1 TB size.

3.3 Analysis of experimental results

The experiment was mainly conducted on the large data sets NTU-RGB + D and Kinetics.The accuracy and loss rate curves of the verification set on the NTURGB+D data set are shown in Figs.6 -13.

Table 1 Experimental platform

Fig.6 X-sub joint accuracy

Fig.7 X-sub joint loss

Fig.8 X-sub bone accuracy

Fig.9 X-sub bone loss

Fig.10 X-view joint accuracy

As shown in Figs 6 -13,in the X-Sub dataset,the accuracy and loss rate of joint flow and bone flow tend to be stable after epoch 30.The final accuracy rate of joint flow is about 0.873,the loss rate is about 0.489,the accuracy rate of bone flow is about 0.869,and the loss rate is about 0.508.Similarly,in the X-View dataset,the accuracy and loss rate of joint flow and bone flow tend to be stable after epoch 30.The final accuracy rate of joint flow is about 0.942, the loss rate is about 0.197,the accuracy rate of bone flow is about 0.938,and the loss rate is about 0.203.

Fig.11 X-view joint los

Fig.12 X-view bone accuracy

Fig.13 X-view bone loss

The recognition accuracy on the validation set of NTU-RGB + D and Kinetics dataset are shown in Table 2 and Table 3.The comparison between the fused results and the results of other models is shown in Table 4.

Table 2 Experimental results under the NTU dataset

Table 3 Experimental results under the Kinetics dataset

Table 4 Comparison of validation accuracy between the proposed method and other methods on NTU-RGB+D and Kinetics dataset

For the NTU-RGB+D dataset,each sample of the dataset has a maximum of 2 people.If there are less than 2 people in the sample,then 0 is used to fill.The maximum number of frames in each sample can be 300; if the number of frames is less than 300,repeat sampling until it reaches 300,the experiment is carried out on the PyTorch platform with stochastic gradient descent (SGD) algorithm.The batch size is 16,the weight attenuation is 0.001,and the learning rate is 0.1.The training will end at the 50th epoch.The experimental results in the two sub datasets of NTU-RGB+D of the method proposed in this paper is shown in Table 2.It can be seen from the Table 2 that the Top-1 accuracy of the joint results and bone results under XSub dataset is 87.3% and 86.9% respectively;the Top-1 accuracy of the joint results and bone results under X-View dataset is 93.7% and 93.8% respectively;and the final Top-1 accuracy of combined results of X-Sub and X-View is 89.5%,95.4% respectively.

For Kinetics dataset,the experimental setup is the same as Ref.[6].SGD algorithm is used in the experiment on PyTorch platform,with batch size of 8,weight attenuation of 0.001,and learning rate of 0.1.The training ends on the 65th epoch.The experimental results of the Kinetics dataset of the method proposed in this paper is shown in Table 3.It can be seen from the Table 3 that the Top-1 accuracy of the joint results and bone results under Kinetics dataset is 34.3% and 34.4% respectively;and the final Top-1 accuracy of combined results of Kinetics is 36.7%.

As shown in Table 4,compared with 2S-AGCN,the result of proposed method is improved by 1.0%,0.3% respectively on X-Sub and X-View which are two subdatasets under NTU-RGB + D dataset,and is improved by 0.6% and 0.2% respectively on Kinetics dataset.The effectiveness of the method is also proved by this.However,compared with graph neural network neural architecture search (GCN-NAS),the accuracy of X-Sub is higher than that of GCN-NAS,while the accuracy of X-View and Kinetics is still not enough.

4 Conclusion

Aiming at the traditional behavior recognition method based on graph convolution,this paper only considers the problem of physical connection or the same type of joints when building the model,Motif-GCN is used to extract the spatial information of human skeleton points,The first Motif is used to encode the edges with natural connection relationship in the human body;the other Motif is used to encode the relationship between joints without connectivity in the human skeleton,and add joint and bone information at the same time.a two-stream structure is constructed,and experiments are carried out on the large dataset NTURGB+D.Finally,the accuracy rates on the two sub datasets X-Sub and X-View are 89.5% and 95.4%respectively,and the experimental results are 1.0%and 0.3% higher than those of the 2S-AGCN model.The method proposed in this paper,by adding the relationship between the joints of non physical connections and by building a two stream structure to add more action information,so as to strengthen the connection between the physical connection and non physical connection,joints in the human skeleton structure and captures the higher-order information.The effectiveness of this method is proved by the improvement of the experimental results compared with the 2S-AGCN model,but compared with some other recent methods,such as GCN-NAS,the experimental results need to be further improved.