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

Claim 2:
3. The method according to claim 2, wherein the step of obtaining the global features ƒcls and the local features ƒƒ_local of occluded pedestrians comprises: generating a feature map sequence xp={xpi|i=1,2, . . . N} by segmenting the obtained the pedestrian features x according to a preset patch size p, wherein N is a separable quantity, and then adding camera perspective information [CAM_VIEW] to xp, the dimension of camera perspective information is the same as xp, as shown in equation(2):

xp=xp+λ*Ecam_view  (2), wherein λ is a hyperparameter representing the weight of the camera perspective information, and Ecam_view is [CAM_VIEW] representing the camera perspective information;
adding [CLS_TOKEN] representing the global feature and the position information coding [POS_TOKEN] to xp, and then feature Z can be obtained after linear coding, as shown in equation (3):

Z=[xcls;linear(xpi)]+Epos  (3)

wherein xcls is the global feature vector [CLS_TOKEN]; linear(·) is a linear coding function; Epos is [POS_TOKEN] representing the spatial position;
inputting the feature Z into the ViT, wherein the ViT is stacked by multiple Transformer blocks, as shown in equation (4) and equation (5):

Zl′=MSA(LN(Zl−1))+Z1−1l=1 . . . L  (4)

Zl=MLP(LN(Zl′))+Zl′l=1 . . . L  (5)

wherein Zl represents the features of the output of the Transformer block in the 1 layer, Zl′ is the intermediate result in the Transformer block, and L is the total number of layers;
MSA(·) is the multi-head attention, LN(·) is the layer normalization, and MLP(·) is the multilayer perceptron;
the network output result is the output feature of the last layer, which are the global features ƒcls ∈ R1×c and the local feature group ƒƒ_local respectively.