Vehicle re-identification method based on double sub-networks

Abstract

The invention discloses a vehicle re-identification method based on double-subnetworks. The method comprises the following five modules: obtaining a vehicle image data set, preprocessing vehicle image data, constructing a vehicle image feature extraction neural network, training a vehicle re-identification model, extracting a to-be-identified vehicle image feature vector, and calculating the feature similarity between the to-be-identified vehicle image feature vector and a test set image. The vehicle re-identification method based on the double sub-networks aims to construct a neural network model with a strong discrimination capability, further solves the influence of factors such as illumination and a visual angle on a vehicle re-identification result, and improves the accuracy of vehicle re-identification. According to the invention, a double-subnetwork is designed on the basis of fusion of CNN and Transform structures, and effective global feature information can be obtained while multi-granularity local features of a vehicle image region are learned cooperatively, so that the discriminative characterization capability of a vehicle re-identification model is improved.

Description

technical field [0001] The invention relates to the technical field of vehicle re-identification, in particular to a dual-subnetwork-based vehicle re-identification method. Background technique [0002] As one of the key technologies of smart city and smart transportation, vehicle re-identification technology has received extensive attention and in-depth research from academia and industry in recent years. Vehicle re-identification, also known as Vehicle Re-identification, is essentially the same as pedestrian re-identification and is a type of re-identification task. This problem can be regarded as an image retrieval problem, which finds the same target vehicle image under the multi-view task of cross-camera scene based on the given vehicle image. [0003] With the rapid development of the field of deep learning, vehicle re-identification technology based on deep learning has also made great progress. The method based on deep learning usually first needs to build a neural...

AI Technical Summary

Problems solved by technology

This type of method ignores that discriminative features may appear anywhere in the vehicle, so it is weak for samples with local subtle differences;

[0007] Based on the method of generative confrontation network, some works use GAN to g

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