Cross-domain target re-identification method based on feature adversarial learning and self-similarity clustering

Abstract

The invention belongs to the field of computer vision and pattern recognition, and particularly relates to a cross-domain target re-identification method, system and device based on feature adversarial learning and self-similarity clustering, and aims to solve the problem of poor robustness of an identification result due to the fact that the number of clustering centers is not fixed and the discrimination of feature expression is limited in an existing target re-identification method. The method of the system comprises the following steps: acquiring a to-be-identified image as an input image;for the input image, extracting features of the input image through a pre-trained feature extraction network to serve as first features; and calculating the Euclidean distance between the first feature and the corresponding feature of each image in the image library, sorting the Euclidean distances, and outputting a sorting result. According to the invention, the robustness of cross-domain targetre-identification is improved.

Description

technical field [0001] The invention belongs to the fields of computer vision and pattern recognition, and in particular relates to a cross-domain target re-recognition method, system and device based on feature confrontation learning and self-similarity clustering. Background technique [0002] Object re-identification is a subproblem in the field of image retrieval. Given an image of an object, usually a pedestrian image or a vehicle image, the object re-identification task aims to find images of the object in other scenes. In recent years, vehicle re-identification and pedestrian re-identification have become the focus of research in the field of computer vision, and many methods based on deep learning have achieved good results. However, most of these deep learning-based methods require a large amount of labeled training data, and due to the existence of domain differences, the performance of a model trained on one data set is tested on another data set, and the perform...

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Problems solved by technology

[0005] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem that the existing target re-identification method has poor robustness of the recognition result due to the unfixed number of cluster centers and the limitation of the discriminative power of feature expression, the first method of the present invention On the one hand, a cross-domain object re-identification method based on feature adversarial learning and self-similarity clustering is proposed, which includes:

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