Vehicle type recognition method based on deep Fisher network

A car model recognition, Fisher's technology, applied in the direction of character and pattern recognition, instruments, computer parts, etc., can solve problems such as the difficulty of optimizing the initial value, achieve the effect of reducing memory consumption, speeding up recognition speed, and improving recognition rate

Inactive Publication Date: 2016-03-16
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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However, these methods are difficult to solve the contradiction between complexity and generalization in pattern recognition. Although neural networks have

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  • Vehicle type recognition method based on deep Fisher network
  • Vehicle type recognition method based on deep Fisher network
  • Vehicle type recognition method based on deep Fisher network

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[0034] In order to describe the technical content, structural features, achieved goals and effects of the present invention in detail, the following will be described in detail in conjunction with the embodiments and accompanying drawings.

[0035] This patent proposes a car model recognition method based on a deep Fisher network, which achieves good results in car model recognition. The schematic diagram of the whole algorithm is shown in figure 1 shown, including steps:

[0036] Step 1: Perform SIFT feature extraction on the image of the vehicle model database as the 0th layer of the Fisher network, as shown in the schematic diagram figure 2 (0);

[0037] SIFT is a local feature descriptor proposed by David Lowe, which has been developed rapidly and widely used. Since the SIFT feature points are extracted by extremum detection in the scale space, they have translation scale invariance; at the same time, a main direction is assigned to each feature point, so the rotation ...

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Abstract

The invention provides a vehicle type recognition method based on a deep Fisher network. Firstly the 0th layer of the Fisher network is constructed, and the SIFT features of each type of vehicle type image are extracted as for a database having K types of vehicle type images; then the 1st layer of the Fisher network is constructed, Fisher vector coding is performed on the extracted SIFT features, the coded vectors are spatially stacked and then L2 normalization and PCA dimension reduction are performed; Fisher vector coding is performed on the obtained feature expressions of the 1st layer, and the 2nd layer of the Fisher network is formed by symbolic square root and L2 normalization processing; finally a global feature expression obtained through different vehicle type images is applied to linear support vector machine training so that a recognition system having K vehicle types is obtained; and as for a vehicle to be recognized, the vehicle to be recognized is enabled to pass the Fisher network to obtain test feature vectors, and the test feature vectors are imported to the recognition system so that the type of the vehicle to be recognized can be recognized.

Description

technical field [0001] The invention belongs to the technical field of pattern classification, and in particular relates to a vehicle identification method based on a deep Fisher network. Background of the invention [0002] Traffic information collection is the basis for building a dynamic traffic information platform for intelligent transportation systems, and vehicle types are an important part of traffic information. Road and bridge, parking lot charging system, road and bridge management and monitoring system, etc. all need vehicle type identification. In the intelligent traffic management system, the vehicle type recognition system can automatically and real-time detect passing vehicles and identify the vehicle type, license plate, and vehicle logo of the traffic management system, which can be widely used in road vehicle information records, expressway automatic toll collection, electronic police Monitoring, parking lot safety management, accident, suspicion, trackin...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/584G06F18/2111G06F18/2411G06F18/214
Inventor 李鸿升刘海军胡欢曹滨范峻铭
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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