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Vehicle attribute recognition method and device based on convolutional neural network

A convolutional neural network and vehicle technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as difficulty in extracting seat belt information, inaccurate detection, and low recognition accuracy, saving computing time. , the effect of improving accuracy and speed

Inactive Publication Date: 2019-12-17
BEIJING INSTITUTE OF TECHNOLOGYGY +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The disadvantages of the vehicle self-attribute recognition method in the above-mentioned prior art are: different features need to be artificially designed for different tasks, and the recognition accuracy is low
The driver’s abnormal behavior detection algorithm in this method only detects whether the seat belt is worn, and in the seat belt detection process, the Hough transform is used to identify the slash information of the seat belt, which is easily interfered by factors such as the driver’s clothing and poor light transmission of the window. Difficulty extracting obvious seat belt information, which leads to inaccurate detection

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  • Vehicle attribute recognition method and device based on convolutional neural network
  • Vehicle attribute recognition method and device based on convolutional neural network

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Embodiment Construction

[0025] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0026] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof. It will be understoo...

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Abstract

The invention provides a vehicle attribute recognition method based on a convolutional neural network. The method mainly includes: using sample images to train a convolutional neural network, obtaining an image of a vehicle to be identified, using the trained convolutional neural network to identify the image of the vehicle, and obtaining the vehicle model, body color, and abnormal driver behavior attributes. The vehicle attribute recognition method provided by the present invention can directly use the pre-trained convolutional neural network to directly extract deep features after obtaining the image of the vehicle to be recognized, without the need for the user to design features, to perform vehicle type recognition, body color recognition and driver recognition. Abnormal behavior identification.

Description

technical field [0001] The invention relates to the technical field, in particular to a method and device for identifying vehicle attributes based on a convolutional neural network. Background technique [0002] Vehicle attributes are important clues for related applications such as vehicle identification, identification, and retrieval. It has rich connotations. It not only identifies the attributes of the vehicle itself, such as the license plate number, vehicle brand, and body color, but also includes occupant attributes, such as the abnormality of the driver. Behavior. Identifying the attributes of vehicles in monitoring data can not only extract useful information from massive data, but also save data storage costs, and play an important role in helping people intelligently analyze vehicles in monitoring data. In addition, more than 3,000 passengers in the world die every day due to traffic accidents, and millions of passengers are injured or even disabled due to traffi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/584G06V20/597G06N3/045G06F18/214G06F18/24
Inventor 王耀威徐博田永鸿黄铁军
Owner BEIJING INSTITUTE OF TECHNOLOGYGY