Pedestrian re-recognition method based on erased salient region

Abstract

The invention discloses a pedestrian re-recognition method based on an erased salient region. The method comprises the steps: (1) randomly selecting P different persons from training data, to randomlyselect K pictures for each person, and obtaining P * K pictures as primary training data; (2) obtaining a salient region of the picture according to the similarity degree between the pictures; (3) erasing a salient region of a certain proportion according to a preset erasing region and probability; (4) extracting features of the picture with the salient region erased, performing pooling operationby utilizing a self-adaptive global pooling layer, and calculating errors by utilizing the features; (5) calculating a feature vector of the original image, and calculating an error by utilizing thefeature vector; (6) returning the gradient training model by combining the errors obtained in the steps (4) and (5); and (7) performing pedestrian re-recognition application by using the trained model. By utilizing the method, the model can be helped to learn more abundant feature expressions, and the re-recognition effect is improved.

Description

technical field [0001] The invention belongs to the field of pedestrian re-identification, in particular to a pedestrian re-identification method based on erasing salient regions. Background technique [0002] Person re-identification is a very important research area, given a picture of a person, it needs to return another picture of this person in the database. Due to the widespread application of deep learning, there has been rapid development and progress in this field in the past few years. However, due to occlusions, changes in pedestrian poses, background effects, and different camera perspectives, pedestrian re-identification is still a very challenging task. Pedestrian re-identification plays a very important role in the field of security. For example, it can query children separated from their parents in amusement parks, and use urban cameras to quickly locate and track criminal suspects. Therefore, how to improve the performance of re-identification has attracte...

AI Technical Summary

Problems solved by technology

However, as the training continues, the model will pay more and more attention to the visual information whose significance is easy to judge, which will cause the model to ignore other visual features and reduce its accuracy, especially when the pedestrians in the two pictures are not clearly distinguished. The effect will be greatly reduced

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