Pedestrian re-identification method based on multi-component self-attention mechanism

The invention provides a pedestrian re-identification method based on a multi-component self-attention mechanism. The method comprises the following steps: firstly, pre-training a deep convolutional neural network backbone model; then, after the backbone model is branched, a multi-component self-attention network is constructed, and multi-component self-attention characteristics are obtained; inputting the multi-component self-attention features into a classifier, and performing joint training to minimize cross entropy loss and metric loss; and finally, inputting a test set picture into the trained model, fusing the output part features to obtain an overall feature, and realizing pedestrian re-identification through metric sorting. According to the method, various challenges existing in the pedestrian re-identification problem are fully considered, a multi-component self-attention mechanism is provided, the attention activation area is effectively expanded, and pedestrian features areenriched; the self-attent