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

Claim 5:
6. The method according to claim 5, wherein obtaining the local feature group ƒkp_en in step (3) comprises:
firstly, the group of features of pedestrian key-points can be expressed as equation (9):

ƒkeypoints={ƒkeypointsi|i=0,1, . . . ,S}  (9)

secondly, apply 1*1 convolution to each key-point feature and the global feature ƒcls, as shown in equation (10) and equation (11):

ƒkp_conv=Conv1×1(ƒkeypoints)  (10)

ƒcls_conv=Conv1×1(ƒcls)  (11)

wherein ƒkp_conv is the feature after convolution of each local feature, and ƒcls_conv is the feature after convolution of global features;
lastly, by using the obtained group of features of pedestrian key-points ƒkeypoints and global features ƒcls, the local feature group of enhanced key-points ƒkp_en is calculated by vector quantity product, softmax and addition, as shown in equation (12) and equation (13):

Vsim=Softmax(ƒkp_conv⊙ƒcls_conv)  (12)

ƒkp_en=Conv(ƒkeypoints+w*(ƒcls_conv+Vsim*ƒcls))  (13)

wherein Cony is the convolution operation; w is the learnable weight; Vsim is the similarity.