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An end-to-end human detection and attribute recognition method

A technology of human body detection and attribute recognition, which is applied in the fields of target detection and human body attribute recognition, and can solve problems such as low model training efficiency and ineffective use of empirical knowledge

Active Publication Date: 2022-07-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of low model training efficiency and ineffective use of empirical knowledge in the existing human body attribute recognition methods. The present invention proposes an end-to-end human body detection and attribute recognition method based on multi-task learning

Method used

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  • An end-to-end human detection and attribute recognition method
  • An end-to-end human detection and attribute recognition method
  • An end-to-end human detection and attribute recognition method

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Experimental program
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Embodiment

[0068] see figure 1 , the specific implementation process of this implementation includes:

[0069] Step 101: Acquire a detection data set containing a human body. Delete the samples that do not contain human objects in the detection data sets containing human objects, retain only the samples containing human objects, and unify the labeling formats of multiple data sets, and randomly arrange to obtain the data set DB 1 .

[0070] Step 102: Obtain a human body attribute recognition data set, and perform attribute alignment on multiple human body attribute recognition data sets, that is, take the union S of the attribute sets of all the data sets, and use the attributes in the union S as the attributes of the integrated data set. Set of attributes, setting default values ​​for attributes that are missing in the dataset, such as -1. Unify the annotation formats of multiple datasets, and randomly arrange to obtain the dataset DB 2 .

[0071] Step 103: For the two data sets ob...

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Abstract

The invention proposes an end-to-end human body detection and attribute recognition method based on deep learning, which aims to improve the network operation efficiency and generalization performance. The network structure is composed of two modules: target detection and human attribute recognition. The target detection module completes the recognition and positioning of human objects. The human body attribute recognition module is a multi-output network, which is used to complete the judgment of multiple human body attributes. The model can accurately detect multiple people in the real scene and detect the attributes of these people. At the same time, combined with the characteristics of the model, a method of using attribute correlation as prior knowledge to guide network training is set up.

Description

technical field [0001] The invention relates to the field of target detection and human body attribute recognition, in particular to the human body attribute recognition in real scenes. Background technique [0002] Human attribute recognition refers to the judgment of human attributes such as gender, age, hairstyle, and clothing of people in real scenes. These attributes have many applications in pedestrian recognition and retrieval. For example, the identity of pedestrians is verified when the video quality is poor; in criminal investigation cases, similar suspects can be retrieved from surveillance videos through the external attributes of criminal suspects. [0003] The existing human attribute recognition methods mainly regard target detection and attribute recognition as two independent tasks, build a deep convolutional neural network for the two tasks respectively, and then connect the two networks in series. This kind of method is relatively simple to implement, but...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/40G06V40/10G06K9/62G06N3/04G06N3/08G06V10/774
CPCG06N3/08G06V40/103G06V20/41G06N3/045G06F18/214
Inventor 陈爱国赵太银朱大勇罗光春谷俊霖杨栋栋
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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