Pedestrian re-identification method based on label uncertainty and a human body component model

A pedestrian re-identification and uncertainty technology, applied in the field of computer vision, can solve problems such as large differences, incomplete and accurate classification confidence of local information, etc., and achieve the effect of improving performance and wide application value.

Active Publication Date: 2019-05-31
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

The effectiveness of this method has been verified and the corresponding recognition rate has been greatly improved, but there are still some shortco

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  • Pedestrian re-identification method based on label uncertainty and a human body component model
  • Pedestrian re-identification method based on label uncertainty and a human body component model
  • Pedestrian re-identification method based on label uncertainty and a human body component model

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[0035] Example

[0036] like figure 1 Shown is a flow chart of a pedestrian re-identification method based on label uncertainty and human component model. The specific steps include:

[0037] (1) Build a deep neural network model based on human components;

[0038] In the step (1), the ResNet-50 network is used as the basic structure to modify and adjust.

[0039] In this embodiment, a deep neural network model for 6 classification tasks based on human body components is constructed.

[0040] The deep neural network construction method is as follows: remove the fully connected layer with an output dimension of 1000 in the ResNet-50 network, and modify the downsampling rate stride=2 in layer4 to stride=1; after the pooling layer is divided into 6 Each part contains a fully connected layer of 256 neurons, a batch normalization layer, a dropout layer, and finally a classification fully connected layer.

[0041] (2) Initialize the constructed deep neural network model, and tra...

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Abstract

The invention discloses a pedestrian re-identification method based on label uncertainty and a human body component model. The pedestrian re-identification method comprises the following steps: (1) constructing a deep neural network model based on a human body component; (2) initializing the constructed deep neural network model, and performing training according to the constructed deep neural network structure to obtain another model; (3) training the initialized deep neural network model and updating parameters in the network; (4) carrying out feature extraction on the target pedestrian image and pedestrian images in a pedestrian image library by adopting the trained deep neural network; And (5) performing cosine similarity calculation and sorting on the extracted features to obtain a recognition result. According to the method, the problems that local component information is not completely accurate and classification confidence coefficients of all local components are large in difference are effectively processed, the correct rate of pedestrian re-identification can be effectively improved, and/or the false identification rate can be effectively reduced.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a pedestrian re-identification method based on label uncertainty and human body component model. Background technique [0002] With the development and progress of deep neural network and computer vision technology, as well as the rapid development of large-scale data storage technology, pedestrian video surveillance can not only improve the level of public safety management, but also protect people's personal and property safety. It is an important means for the state to ensure the quality of life of the people. Pedestrian video surveillance can intelligently search for specific pedestrians in large-scale image and video data. With the development and progress of application requirements and technology, person re-identification has become a challenging and practical research hotspot in the field of computer vision. [0003] Person re-identification technology has developed from ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
Inventor 何颖丁长兴王侃
Owner SOUTH CHINA UNIV OF TECH
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