Patent ID: 11954599
Assignee: INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. The storage apparatus according to claim 9, wherein the method further includes a method for obtaining the pre-trained pedestrian search model through training comprising:
obtaining a data set of training sample images, wherein the data set of training sample images comprises scene images; and normalizing each scene image to obtain normalized scene images;
extracting a feature of each normalized scene image based on a conv1 layer, a conv2 layer, a conv3 layer, and a conv4 layer of the residual network, and obtaining candidate regions based on a region proposal network (RPN) layer of the residual network;
extracting a feature of each candidate region based on a region of interest (ROI)-pooling layer of the residual network, performing pooling on the feature of each candidate region based on a conv5 layer and a global pooling layer of the residual network to obtain a pooled feature, and using the pooled feature as a first feature;
cutting out an image of each candidate region from the normalized scene image, and normalizing the image; and obtaining, after normalization, a pooled feature of each candidate region by using an instance-aware network and using the pooled feature of each candidate region as a second feature, wherein the instance-aware network is constructed based on a pedestrian search model by deleting the RPN layer from the pedestrian search model, retaining the new classification layer in the classification and regression layer, and deleting an original classification and regression layer;
obtaining a first classification probability of each target pedestrian based on each first feature by the new classification layer in the pedestrian search model, and using the first classification probability as a first probability; and obtaining a second classification probability of each target pedestrian based on each second feature by a classification layer of the instance-aware network, and using the second classification probability as a second probability;
calculating a first loss value between the first feature and the second feature by a preset first loss function; and calculating a second loss value between the first probability and the second probability by a preset second loss function; and
calculating a global loss value based on the first loss value and the second loss value by using a preset third loss function, and updating network parameters of the pedestrian search model based on the global loss value.