Patent ID: 11908222
Assignee: HANGZHOU DIANZI UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 7:
8. The method according to claim 7, wherein the specific implementation process of step (4) comprises steps of step 4-1 to step 4-4:
step 4-1: performing global average pooling to the heat map of the key-points ƒpos to obtain the features of pedestrian key-points, and supplementing the features of pedestrian key-points to the local features ƒƒ_local;
step 4-2: using the local feature map ƒlocal as a graph structure, wherein the graph comprises H*W nodes and each node is a C-dimensional feature, inputting the local feature map ƒlocal into two 1*1 convolutional networks, and then transposing the output of one of the convolutional networks to construct the relationship between nodes, as shown in equation (15):

Ri,j=Conv(ƒlocal)TConv(ƒlocal)  (15)

wherein Ri,j is the matrix of relational feature, and Conv is a convolutional network;
step 4-3: obtaining the features of spatial perception fs, of the corresponding relationship by using the matrix of relationship feature Ri,j, and then embedding the local feature map ƒlocal, the features of pedestrian key-points ƒpos and the features of spatial perception fs, into a link, as shown in equation (16) and equation (17):

ƒsp=Conv(Ri,j)  (16)

ƒconcat=Concat[Conv(ƒlocal),Conv(ƒsp), Conv(ƒpos)] (17) wherein ƒsp are the features of spatial perception, Concat(·) is the channel link function, and ƒconcat are the connected feature vectors;
inputting the ƒconcat into a 1*1 convolutional network and Sigmod to obtain a spatial attention map fatten, and finally a final pedestrian feature map ƒatt_local is obtained by multiplying the spatial attention map fatten with the local feature map ƒlocal;
step 4-4: constructing multiple classification heads according to the pedestrian structure, and dividing the pedestrian feature map ƒatt_local into four local features, ƒ1, ƒ2, ƒ3, ƒ4, to classify the pedestrian images.