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Multi-feature fusion vehicle re-identification method based on deep learning

A multi-feature fusion, deep learning technology, applied in the field of high-precision vehicle recognition

Active Publication Date: 2018-02-23
BEIHANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In order to overcome the deficiencies of the prior art, the present invention provides a multi-feature fusion vehicle re-identification method based on deep learning, which can handle both the situation where the license plate information exists and the situation where the license plate information cannot be obtained

Method used

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Embodiment Construction

[0068] The specific steps of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0069] The invention proposes a deep learning-based multi-feature fusion vehicle re-identification method. combine figure 1 It can be seen from the overall schematic diagram of the method that the vehicle re-identification process of the present invention is mainly divided into five parts, which are respectively the training model stage, the license plate recognition stage, the vehicle recognition stage, the similarity measurement stage and the result visualization stage.

[0070] A. Training model stage: The model trained in this stage is mainly used to extract expressive feature vectors in the vehicle recognition stage. The specific steps are as follows:

[0071] First, load the pre-trained vehicle classification network model as the pre-training model used in the present invention;

[0072] Second, according to figure 2 Th...

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Abstract

The invention discloses a multi-feature fusion vehicle re-identification method based on deep learning. The method comprises five parts of model training, license plate identification, vehicle identification, similarity measurement and visualization. The method comprises the steps that a large-scale vehicle data set is used for model training, and a multi-loss function-phased joint training policyis used for training; license plate identification is carried out on each vehicle image, and a license plate identification feature vector is generated according to the license plate recognition condition; a trained model is used to extract the vehicle descriptive features and vehicle attribute features of an image to be analyzed and an image in a query library, and the vehicle descriptive features and the license plate identification vector are combined with the unique re-identification feature vector of each vehicle image; in the stage of similarity measurement, similarity measurement is carried out on the image to be analyzed and the re-identification feature vector of the image in the query library; and a search result which meets requirements is locked and visualized.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and more specifically, relates to a multi-feature fusion vehicle re-identification method based on deep learning, which is a feature extraction framework based on deep learning and integrates license plate identification features, vehicle global features, and vehicle interests. A high-precision vehicle recognition method based on regional local features and vehicle attribute features. The invention can not only process images of vehicles without license plate information, but also provide various attribute information of arbitrary vehicles. Background technique [0002] Visual tracking and object detection is an early research direction in the field of computer vision. After decades of accumulation, these two directions have achieved remarkable development. However, the relevant research on vehicle re-identification started late, and there are relatively few domestic and foreign researc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/34G06K9/62
CPCG06V20/584G06V30/153G06V20/625G06F18/22G06F18/214G06F18/253
Inventor 周忠吴威姜那刘俊琦孙晨新
Owner BEIHANG UNIV
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