Local feature and deep learning-based gateway vehicle retrieval system and method

A local feature, deep learning technology, applied in the field of bayonet vehicle retrieval system, can solve problems such as inaccuracy, not reaching the level of fine-grained vehicle retrieval, etc.

Active Publication Date: 2018-06-22
ZHEJIANG YINJIANG RES INST
View PDF13 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] "Deep Learning-Based Vehicle Vehicle Identification Model Construction Method and Vehicle Vehicle Identification Method", application number 20161...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Local feature and deep learning-based gateway vehicle retrieval system and method
  • Local feature and deep learning-based gateway vehicle retrieval system and method
  • Local feature and deep learning-based gateway vehicle retrieval system and method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0102] Embodiment: a kind of vehicle picture retrieval method based on local feature and deep learning, comprises the steps:

[0103] (1) extracting the image features of the image to be detected;

[0104] Image features can be global features or regional features, and can be extracted using deep neural networks or SIFT, SURF and other methods. The deep neural network includes Alexnet network, vgg network, GoogleNet network, etc. Specifically, the classic vgg16 network is used to extract vehicle features, the loss function is jointly trained with softmax and triple loss, and the last fully connected layer is extracted. The vector of 1000*1 dimension is used as global features. The SIFT method is used to extract the features of vehicle annual inspection marks and lights as local features.

[0105] (2) The product of the image feature of the image to be detected and the overall weight matrix is ​​obtained to obtain the binary feature code of the image to be detected;

[0106]...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a local feature and deep learning-based gateway vehicle retrieval system and method. According to the system and method, global features of vehicles are extracted by utilizinga deep neural network; a network model is trained by adoption of loss functions of softmax loss and triple loss; annual inspection mark features and vehicle lamp features are extracted to obtain local feature vectors; and finally the local feature vectors are weighted and combined, and global feature vectors of the last full-connection layer of the neural network are utilized to serve as vehiclefeatures to carry out retrieval. During the retrieval, an improved k-means algorithm is adopted to find K classes, and hash functions are formed by utilizing a SVM so as to carry out Hamming code encoding, so that the retrieval speed and the retrieval precision are improved and the storage space is saved.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a checkpoint vehicle retrieval system and method based on local features and deep learning. Background technique [0002] With the development of society, intelligent traffic monitoring in the field of intelligent transportation is a very important development direction at present. At present, a large number of electronic police and checkpoint systems have been deployed on urban roads in my country. These systems can capture high-definition pictures of vehicles in real time, and identify and analyze the license plate number, as well as some vehicle model information (such as vehicle size, color, etc.). However, in the currently used bayonet monitoring system, the license plate number recognition still has a misrecognition and missed recognition rate of about 10%. More importantly, it will not be possible to identify illegal vehicles with license plates or deliberately c...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06F17/30
CPCG06F16/583G06V20/52G06F18/2411G06F18/214
Inventor 温晓岳田玉兰陈涛李建元
Owner ZHEJIANG YINJIANG RES INST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap