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Multi-person behavior recognition system based on key point detection and working method

A technology of recognition system and working method, applied in character and pattern recognition, instrument, calculation, etc., can solve the problems of reducing the efficiency of recognition, omission of detection, failure of classification and recognition, etc., and achieve the effect of improving the accuracy rate

Active Publication Date: 2020-03-27
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As the number of people in the field of view increases, the calculation amount of this method will increase exponentially, which greatly reduces the efficiency of recognition.
And there will be a problem of missing detection, that is, the person is not detected in the field of vision, which will directly cause the failure of subsequent classification and recognition
It can be seen that although this top-down recognition method can detect the gestures of multiple people and perform behavior classification, the recognition effect is not ideal.

Method used

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  • Multi-person behavior recognition system based on key point detection and working method
  • Multi-person behavior recognition system based on key point detection and working method
  • Multi-person behavior recognition system based on key point detection and working method

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0041] In one or more embodiments, a multi-person behavior recognition system based on key point detection is disclosed, such as figure 1 shown, including:

[0042] (1) The original image preprocessing module is used to adjust the format of the input image frame, filter and denoise the input image. Adjusting the format of the input image frame includes adjusting the size of the input image, adjusting the gray value of the input image, and adjusting the storage format of the input image; the input image is denoised by a filter, and then the irrelevant information in the image is eliminated by a smooth denoising operation impact on subsequent outcomes;

[0043] (2) Key point detection module, refer to image 3 , including:

[0044] ① The key point feature extraction unit performs SIFT and SURF key point feature extraction on each frame of image to obtain the key point feature matrix;

[0045] Specifically, a Gaussian function is used to establish a scale space for each frame o...

Embodiment 2

[0060] In one or more implementations, a method of multi-person behavior recognition based on key point detection is disclosed, such as figure 2 shown, including the following steps:

[0061] Step S01: Raw image preprocessing operation

[0062] Input two multi-person behavior datasets, MPII Human Pose Dataset and MSCOCO Dataset, with a total of 25k image frames, including several marked image frames, and perform filtering and denoising operations on the image frames of the dataset. The image frames in the data set are processed in gray scale, and the format of the network input is adjusted appropriately.

[0063] In this embodiment, the purpose of selecting MPII Human Pose Dataset and MSCOCO Dataset data sets for the original image is to facilitate the description of the method of this embodiment. In the actual application process, the original image is the collected image to be classified and recognized.

[0064] Step S02: Extract SIFT and SURF features of the image frame ...

Embodiment 3

[0113] In this embodiment, the real-time class behavior of classroom students is taken as an example. The classroom camera equipment collects the class situation of the students in the current classroom, and analyzes the class status of the students in this period of time in real time. The steps are as follows:

[0114] Step S01: Acquired image preprocessing operation

[0115] The camera equipment in the classroom uploads one frame of image to the host computer per second, and performs preprocessing operations on the collected images;

[0116] Step S02: Extract SIFT and SURF features of the image frame

[0117] The collected image is used as input to extract SIFT features. The specific steps are as follows:

[0118] 1) Use the Gaussian function G(x,y,σ) to establish the scale space σ, and the calculation formula of the Gaussian function is shown in (Ι):

[0119]

[0120] Among them, G(x, y, σ) represents a Gaussian function, (x, y) is a spatial coordinate, and σ is a scal...

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Abstract

The invention discloses a multi-person behavior recognition system based on key point detection and a working method. The multi-person behavior recognition system comprises an image preprocessing module which is configured to carry out the preprocessing of a collected image, a key point detection module which is configured to extract key point features of each frame of image, judging the number ofhuman bodies contained in the image and the number of joint points contained in each person through the human body key point prediction matrix, detecting whether two adjacent articulation points areon the same limb or not, connecting any two adjacent articulation points of the same person to obtain a direction vector, and sequentially connecting the articulation points on the same limb to obtainan articulation point vector matrix, and an attitude estimation module which is configured to perform attitude judgment according to the articulation point vector matrix. By analyzing and processingthe multi-person behavior information input into the original image, multi-person posture estimation and current behavior classification and recognition are achieved, and the defect that an existing single-person behavior recognition method cannot detect behaviors of multiple persons in an actual scene is overcome.

Description

technical field [0001] The invention relates to the technical field of computer vision and pattern recognition, in particular to a multi-person behavior recognition system and working method based on key point detection. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Computer vision is a discipline that studies how computers can perceive information from the outside world like the human visual system, that is, to make computers understand the world, and image-based human behavior recognition is a research hotspot in the field of computer vision. Image detection and recognition are basic tasks in computer vision. Image recognition is a technology that allows computers to perform a series of processing and analysis on images, and then recognize targets and objects in various situations. Image recognition technology has gradually developed ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/40
CPCG06V40/20G06V10/30G06V10/462
Inventor 许宏吉李梦荷赵文杰张贝贝石磊鑫
Owner SHANDONG UNIV