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Human body part analyzing method and system, equipment and storage medium

An analysis method and human body technology, applied in the field of computer vision, can solve problems such as low analysis accuracy, and achieve the effect of reducing the difficulty of analysis, improving the analysis accuracy, and reducing randomness

Active Publication Date: 2018-09-28
BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is to overcome the defects of low analysis accuracy in the prior art when analyzing human body objects in an image containing multiple people and obtaining the human body parts of each human body object, and the purpose is to provide a multi-person Human body part parsing method, system, device and storage medium of human image

Method used

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  • Human body part analyzing method and system, equipment and storage medium
  • Human body part analyzing method and system, equipment and storage medium
  • Human body part analyzing method and system, equipment and storage medium

Examples

Experimental program
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Embodiment 1

[0079] Such as figure 1 As shown, the human body part parsing method based on multi-person images of this implementation includes:

[0080] S101. Extracting a first feature map with high-level semantic information from a multi-person image;

[0081] Among them, the high-level semantic information is used to represent the overall information in the multi-person image, such as the objects in the image, the ongoing actions of the objects, and the overall scene information.

[0082] S102. Obtain a plurality of first human body regions of interest according to the first feature map;

[0083] S103. For each first human body region of interest, select a target human body object from the first human body region of interest, and expand the first human body region of interest into a second human body region of interest;

[0084] Wherein, the relatively fixed position of each target human body object in the corresponding second human body interest region makes the target human body obj...

Embodiment 2

[0091] Such as figure 2 and image 3 As shown, this embodiment is further improved on the basis of embodiment 1, specifically:

[0092] Step S101 specifically includes:

[0093] S1011. Use Deeplab v2 (a model for image semantic segmentation formed by using a deep convolutional network) to obtain a first feature map with high-level semantic information in a multi-person image. Specifically, based on the first five convolutional layers of the deep convolutional network, the first feature map is obtained;

[0094] The high-level semantic information includes at least one of color features, texture features, shape features and spatial relationship features in the image.

[0095] Step S102 specifically includes:

[0096] S1021. According to the first feature map, use RPN (Region Proposal Network, region proposal network) to acquire a first human body region of interest.

[0097] Specifically, the principle of using the region proposal network to obtain the region of interest ...

Embodiment 3

[0152] Such as Figure 4 As shown, the system of human body parts analysis based on multi-person images in this embodiment includes a first feature map acquisition module 1, a first area acquisition module 2, a second area acquisition module 3, a second feature map acquisition module 4 and a first feature map acquisition module. Analysis module 5.

[0153] The first feature map acquisition module 1 is used to extract the first feature map with high-level semantic information from the multi-person image;

[0154] Among them, the high-level semantic information is used to represent the overall information in the multi-person image, such as the objects in the image, the ongoing actions of the objects, and the overall scene information. The first region obtaining module 2 is used to obtain a plurality of first human body regions of interest according to the first feature map;

[0155] The second region acquiring module 3 is used for selecting a target human body object from the ...

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Abstract

The invention discloses a human body part analyzing method and system, equipment and a storage medium. The human body part analyzing method comprises the following steps of: extracting a first characteristic pattern with high-level semantic information from a multi-person image; acquiring a plurality of first human body regions of interest according to the first characteristic pattern; respectively selecting a target human body object from each first human body region of interest and expanding each first human body region of interest into a second human body region of interest; down sampling the second human body regions of interest to obtain a second characteristic pattern; and analyzing human body parts of the target human body objects in the second characteristic pattern by adopting a full convolution network to obtain first human body part analyzing results of the target human body objects. The method disclosed by the invention has the advantages that the randomness of human body positions is reduced; the difficulty in analyzing the human body parts is reduced; and the accuracy of analyzing the human body parts of each human body object in the multi-person image is increased atthe same time.

Description

technical field [0001] The present invention relates to the technical field of computer vision, in particular to a method, system, device and storage medium for analyzing human body parts based on multi-person images. Background technique [0002] In daily life, multi-person scenes are ubiquitous, such as family gatherings, birthday parties, wedding scenes, school opening ceremonies, and so on. The semantics of multiplayer scenes are mostly complex, and the application of parsing the specific human objects and their specific body parts in these multiplayer scenes is becoming more and more extensive. For example, in the field of security, the multi-person component analysis method can assist pedestrian re-identification technology to perform automated and refined analysis of surveillance videos. In the field of smart home, the multi-person component analysis method belongs to audio and video technology, combined with automatic control technology, network communication techno...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/32G06K9/46
CPCG06V40/161G06V40/10G06V10/245G06V10/25G06V10/462
Inventor 林嘉刘偲翁志陈宇
Owner BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD
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