Pedestrian re-identification method based on spatio-temporal joint model of residual attention mechanism and device thereof

A device, comprising:

• a first module, configured to perform feature extraction for an input pedestrian x with a ResNet-50 model obtained through pre-training so as to obtain a feature matrix denoted as f;
• a second module, configured to construct a residual attention mechanism network with a network structure comprising a residual attention mechanism module, a feature sampling layer, a global average pooling layer and a local feature connection layer;
• a third module, configured to obtain the feature matrix f with dimensions being H×W×C, take the feature matrix f as an input of the residual attention mechanism network, and take corresponding identity information y as a target output, wherein H, W, C refer to a length, a width and a channel number of a feature map respectively, and further configured to perform channel averaging for each spatial position of the feature matrix f as a spatial weight matrix according to the residual attention mechanism module, activate the spatial weight matrix by softmax to ensure that a convolution kernel learns different features, and calculate an attention mechanism map MSA to obtain a feature matrix FRSA with dimensions being H×W×C by FRSA=f*MSA+f;
• a fourth module, configured to sample the feature matrix FRSA with dimensions being H×W×C into local feature matrixes (FRSA1, FRSA2 . . . , FRSA6) with dimensions being
• H
• 6
• ×
• W
• ×
• C
• by the feature sampling layer and calculate local feature vectors (VRSA1, VRSA2 . . . , VRSA6) by the global average pooling layer;
• a fifth module, configured to connect the local features into a feature vector VRSA by the local feature connection layer and calculate a cross entropy loss between the feature vector VRSA and the pedestrian identity y to obtain the trained residual attention mechanism network after training;
• a sixth module, configured to obtain feature vectors VRSA-α and VRSA-β corresponding to tested pedestrian images xα and xβ respectively according to the trained residual attention mechanism network obtained in e), and calculate a feature distance based on a cosine distance and denote the feature distance as a visual probability PV;
• a seventh module, configured to perform modeling for a spatio-temporal probability according to camera ID and frame number information in a pedestrian tag of a training sample and calculate the spatio-temporal probability PST according to the obtained spatio-temporal model;
• and
• an eighth module, configured to calculate a final joint spatio-temporal probability by using the visual probability PV obtained by the sixth module and the spatio-temporal probability PST obtained by the seventh module to obtain a pedestrian re-identification result.