Unsupervised pedestrian re-identification method based on transfer learning

Abstract

The invention discloses an unsupervised pedestrian re-identification method based on transfer learning, and the method comprises the following steps: 1), pre-training a CNN model on a source data setwith a label, and employing cross entropy loss and ternary metric loss as a target optimization function; 2) extracting pedestrian characteristics of the label-free target data set; 3) calculating a feature similarity matrix by combining the candidate column distance and the absolute distance; 4) performing density clustering on the similarity matrix, setting a label for each feature set with thedistance smaller than a preset threshold value, and recombining the feature sets into a target data set with the label; 5) training the CNN model on the recombination data set until convergence; 6) repeating the steps 2)-5) according to a preset number of iterations, and 7) inputting the test pictures into the model to extract features, and sorting the test pictures according to feature similarityto obtain a result. The source domain labeled data and the target domain unlabeled data are reasonably applied, the accuracy of pedestrian re-identification is improved in the target domain, and thestrong dependence on the labeled data is reduced.

Description

technical field [0001] The invention relates to the fields of artificial intelligence and security monitoring, in particular to an unsupervised pedestrian re-identification method based on transfer learning. Background technique [0002] With the continuous growth of urban population, people pay more and more attention to social and public security issues. At present, many public places are covered with large-scale camera networks, which provide an important infrastructure for monitoring security. In order to enhance the security level and quality of the camera network, pedestrian re-identification technology has also received extensive attention in personnel search. The current pedestrian re-identification method mainly trains a stable and reliable model based on a large amount of labeled video image data in a specific scene. This type of supervised learning method ignores the adaptability of the system to new scenes in practical applications and relies on a large number of...

AI Technical Summary

Problems solved by technology

However, the current migration learning mainly solves the problem of common categories in different data domains. Pedestrian re-identification often does not have the same pedestrians in different scenarios, so it is difficult to directly apply to solve the problem of pedestrian re-identification.

Images