Occluded pedestrian re-identification method based on pose estimation and background suppression

An occluded pedestrian re-identification method based on pose estimation and background suppression, comprising steps of:

• step (1):
• constructing a pedestrian feature extraction backbone network based on ViT to obtain global features ƒcls and local features ƒƒ_local of occluded pedestrians, and recombining the local features ƒƒ_local into a local feature map ƒlocal;
• step (2):
• obtaining a heat map of key-points of a pedestrian images ƒpos and a group of key-point confidences Vkc according to a pre-trained ViTPose, and then obtaining a group of features of pedestrian key-points ƒkeypoints by using the local feature map ƒlocal and the heat map ƒpos;
• step (3):
• constructing a graph convolutional module of local feature enhancement composed of Conv and GCN, and then obtaining a local feature group ƒkp_en by using the global features ƒcls to enhance each key-point feature in the group of features of pedestrian key-points ƒkeypoints according to through Conv, and obtaining an adjacency matrix of key-points A through the key-points, and finally the local feature group ƒkp_en and the adjacency matrix of key-points A are used as input of GCN to obtain final features of pedestrian key-points ƒƒ_keypoints;
• step (4):
• constructing an attention background suppression module composed of global average pooling and convolutional networks, inputting the local feature map ƒlocal obtained by step (1) and the heat map ƒpos obtained by step (2) into the attention background suppression module to output pedestrian features ƒatt_local, segmenting the pedestrian features as final features final features;
• step (5):
• constructing an occluded pedestrian re-identification model by the pedestrian feature extraction backbone network, the ViTPose, the graph convolutional module of local feature enhancement and the attention background suppression module, and then training the occluded pedestrian re-identification module by using the global pedestrian features ƒcls, the features of pedestrian key-points ƒƒ_keypoints and the pedestrian features ƒatt_local to obtain a final occluded pedestrian re-identification model.