A vehicle re-identification method and system

Abstract

The present disclosure provides a vehicle re-identification method and system, including: according to the three-dimensional model of each type of vehicle, rotate the angle to obtain the picture of each angle, extract the key point information of each type of vehicle and the outer contour information of the type of vehicle, and calculate the rotation Angle; input the key point information of each vehicle type, the outline information of the vehicle type and the rotation angle into the vehicle re-identification model to obtain a trained vehicle re-identification model; input the pictures or videos of the vehicle to be tested into the trained vehicle re-identification model In the identification model, the vehicle model to be tested is output.

Description

technical field [0001] The disclosure belongs to the field of computer vision, and in particular relates to a vehicle re-identification method and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The widespread deployment of traffic cameras provides the possibility of video analysis for applications such as logistics, transportation, and smart cities. The key issue in this analysis is to correlate targets across cameras. Although both pedestrians and vehicles are common objects in smart city applications, there has been much attention paid to pedestrian re-identification in recent years. This is because there is a large amount of annotated pedestrian data in pedestrian re-identification, and computer vision research on human faces and bodies is relatively mature. Vehicle re-ID is more challenging than pedestrian re-ID. The speci...

AI Technical Summary

Problems solved by technology

[0006] Methods based on sensors or artificially designed features are relatively complex and have a low recognition rate; methods using multi-dimensional information are sensitive to the special appearance of vehicles, but are easily affected by changes in viewing angle and illumination; methods based on metric learning have a higher recognition rate , the recognition efficiency of difficult samples is also relatively good, but the training time is relatively long; some scholars use feature learning or distance metric learning to train deep neural networks, but the effect of such methods in vehicle re-identification is far less than that of pedestrians. identify

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