CN111860118A - Human behavior analysis method based on artificial intelligence - Google Patents
Human behavior analysis method based on artificial intelligence Download PDFInfo
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- CN111860118A CN111860118A CN202010494775.4A CN202010494775A CN111860118A CN 111860118 A CN111860118 A CN 111860118A CN 202010494775 A CN202010494775 A CN 202010494775A CN 111860118 A CN111860118 A CN 111860118A
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Abstract
The invention discloses a human behavior analysis method based on artificial intelligence. The invention comprises the following steps: acquiring and storing image data acquired by shooting equipment in real time; carrying out feature recognition on the acquired image data; after the human body contour is accurately positioned after the characteristic identification, all behavior characteristics of the human body are obtained by utilizing a characteristic extraction algorithm; constructing a feature model by using the extracted behavior features; inputting the characteristic model into a convolutional neural network, and mining and analyzing the image data by using the convolutional neural network to obtain and store a human body behavior analysis result. According to the invention, a large amount of human body image data are collected as a training set and a test set, human body characteristics are identified by combining an artificial intelligent convolutional neural network, an optimal image is obtained, the optimal image is used as a reference standard to judge and identify human body behavior characteristics, a threshold is set to diagnose and early warn the human body behavior characteristics, and the human body behavior analysis strength and analysis efficiency are improved.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a human behavior analysis method based on artificial intelligence.
Background
Artificial Intelligence (Artificial Intelligence), abbreviated in english as AI. The method is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence, a field of research that includes robotics, language recognition, image recognition, natural language processing, and expert systems, among others.
Generally, behavior feedback of a user has an instructive effect on optimization and performance improvement of a product, and by analyzing behavior logs and user feedback of the user, related personnel can be helped to know the defects and the influence surfaces of the product on experience and performance, so that the analysts are helped to know the use scenes and habits of real users, and a product can bring better product experience and the like.
In the related technology, the user behavior monitoring is carried out for word level analysis in the word document, the user interaction condition of a real product cannot be simulated, the analysis efficiency is low, and the period is long.
Disclosure of Invention
The invention aims to provide a human behavior analysis method based on artificial intelligence.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a human behavior analysis method based on artificial intelligence, which comprises the following steps:
step S1: acquiring and storing image data acquired by shooting equipment in real time;
step S2: carrying out feature recognition on the acquired image data;
step S3: after the human body contour is accurately positioned after the characteristic identification, all behavior characteristics of the human body are obtained by utilizing a characteristic extraction algorithm;
step S4: constructing a feature model by using the extracted behavior features;
step S5: inputting the characteristic model into a convolutional neural network, and mining and analyzing image data by using the convolutional neural network to obtain and store a human behavior analysis result;
step S6: generating a visual report according to the human behavior analysis result and storing the visual report;
step S7: and performing data interpretation on the visual report to obtain and store a data interpretation result, and performing human behavior diagnosis and early warning according to the interpretation result.
Preferably, in step S2, the noise reduction processing is performed on the acquired image data; the noise reduction processing is used for removing a background image of a person and positioning the outline of the person from the background.
Preferably, in the step S3, in the feature extraction, the behavior of the human body is saved in the feature database by using a binary method or a gray scale map method, and the human body feature profile is extracted.
Preferably, in step S4, the step of constructing the feature model includes:
step S41: calling a human body characteristic database to evaluate the image data to obtain an evaluation result;
step S42: adjusting the shooting angle and the shooting focal length of the shooting equipment according to the evaluation result to obtain an optimal image;
step S43: calling an analysis database to analyze the human body position parameters and the change conditions of the optimal image to obtain the human body characteristic information of the optimal image;
step S44: and calling a preset feature point analysis database, and analyzing the human body feature image in the optimal image by combining the human body feature database to obtain the feature points of the human body image in the optimal image.
Preferably, in step S6, the human behavior analysis result generates a human behavior pie chart and a human behavior statistical chart, and the human behavior pie chart and the human behavior statistical chart are stored in a preset visual report database.
Preferably, in step S7, the human behavior pie chart and the human behavior statistical chart are input into a convolutional neural network, the convolutional neural network is used to interpret the human behavior pie chart and the human behavior statistical chart, and determine whether the human characteristics exceed a predetermined threshold, and if the human characteristics exceed the predetermined threshold, the human behavior is obtained and an early warning is sent to the management platform.
The invention has the following beneficial effects:
according to the invention, a large amount of human body image data are collected as a training set and a test set, human body characteristics are identified by combining an artificial intelligent convolutional neural network, an optimal image is obtained, the optimal image is used as a reference standard to judge and identify human body behavior characteristics, and a threshold is set to diagnose and early warn the human body behavior characteristics, so that the human body behavior analysis strength and analysis efficiency are improved, and early warning is conveniently and quickly made.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a step diagram of a human behavior analysis method based on artificial intelligence according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a human behavior analysis method based on artificial intelligence, comprising the following steps:
step S1: acquiring and storing image data acquired by shooting equipment in real time;
step S2: carrying out feature recognition on the acquired image data;
step S3: after the human body contour is accurately positioned after the characteristic identification, all behavior characteristics of the human body are obtained by utilizing a characteristic extraction algorithm;
step S4: constructing a feature model by using the extracted behavior features;
step S5: inputting the characteristic model into a convolutional neural network, and mining and analyzing image data by using the convolutional neural network to obtain and store a human behavior analysis result;
step S6: generating a visual report according to the human behavior analysis result and storing the visual report;
step S7: and performing data interpretation on the visual report to obtain and store a data interpretation result, and performing human behavior diagnosis and early warning according to the interpretation result.
In step S2, noise reduction processing is performed on the acquired image data; the noise reduction processing is used for removing background images of people, and background images such as walls, flowers and plants, the ground and the like are scratched from the images, so that the characteristic outline of a human body can be accurately identified by utilizing characteristic identification, and the character outline is positioned from the background.
In step S3, the behavior of the human body is accurately stored in the feature database by using a binary method or a gray scale map method in the feature extraction, and the human body feature profile is extracted so as to be compared with the behavior of another person.
In step S4, the step of constructing the feature model is as follows:
step S41: calling a human body characteristic database to evaluate the image data to obtain an evaluation result;
step S42: adjusting the shooting angle and the shooting focal length of the shooting equipment according to the evaluation result to obtain an optimal image;
step S43: calling an analysis database to analyze the human body position parameters and the change conditions of the optimal image to obtain the human body characteristic information of the optimal image;
step S44: and calling a preset feature point analysis database, and analyzing the human body feature image in the optimal image by combining the human body feature database to obtain the feature points of the human body image in the optimal image.
In step S6, the human behavior analysis result generates a human behavior pie chart and a human behavior statistical chart, and the human behavior pie chart and the human behavior statistical chart are stored in a preset visual report database.
In step S7, the human behavior pie chart and the human behavior statistical chart are input into a convolutional neural network, the convolutional neural network is used to interpret the human behavior pie chart and the human behavior statistical chart, and determine whether the human characteristics exceed a predetermined threshold, and if the human characteristics exceed the predetermined threshold, an early warning is sent to the management platform for obtaining the human behavior.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In addition, it is understood by those skilled in the art that all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing associated hardware, and the corresponding program may be stored in a computer-readable storage medium.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A human behavior analysis method based on artificial intelligence is characterized by comprising the following steps:
step S1: acquiring and storing image data acquired by shooting equipment in real time;
step S2: carrying out feature recognition on the acquired image data;
step S3: after the human body contour is accurately positioned after the characteristic identification, all behavior characteristics of the human body are obtained by utilizing a characteristic extraction algorithm;
step S4: constructing a feature model by using the extracted behavior features;
step S5: inputting the characteristic model into a convolutional neural network, and mining and analyzing image data by using the convolutional neural network to obtain and store a human behavior analysis result;
step S6: generating a visual report according to the human behavior analysis result and storing the visual report;
step S7: and performing data interpretation on the visual report to obtain and store a data interpretation result, and performing human behavior diagnosis and early warning according to the interpretation result.
2. The human behavior analysis method based on artificial intelligence of claim 1, wherein in step S2, the collected image data is subjected to noise reduction processing; the noise reduction processing is used for removing a background image of a person and positioning the outline of the person from the background.
3. The human behavior analysis method based on artificial intelligence as claimed in claim 1, wherein in the step S3, behavior of human body is stored in the feature database and human feature profile is extracted by using binary method or gray-scale map method in feature extraction.
4. The human behavior analysis method based on artificial intelligence as claimed in claim 1, wherein in the step S4, the step of constructing the feature model is as follows:
step S41: calling a human body characteristic database to evaluate the image data to obtain an evaluation result;
step S42: adjusting the shooting angle and the shooting focal length of the shooting equipment according to the evaluation result to obtain an optimal image;
step S43: calling an analysis database to analyze the human body position parameters and the change conditions of the optimal image to obtain the human body characteristic information of the optimal image;
step S44: and calling a preset feature point analysis database, and analyzing the human body feature image in the optimal image by combining the human body feature database to obtain the feature points of the human body image in the optimal image.
5. The human behavior analysis method based on artificial intelligence of claim 1, wherein in step S6, the human behavior analysis result generates a human behavior pie chart and a human behavior statistical chart, and stores the human behavior pie chart and the human behavior statistical chart into a preset visual report database.
6. The human behavior analysis method based on artificial intelligence of claim 1 or 5, wherein in step S7, the human behavior pie chart and the human behavior statistical chart are input into a convolutional neural network, the convolutional neural network is used to interpret the human behavior pie chart and the human behavior statistical chart, and determine whether the human characteristics exceed a predetermined threshold, if so, the human behavior is obtained and an early warning is sent to the management platform.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113315649A (en) * | 2021-04-21 | 2021-08-27 | 重庆科创职业学院 | Communication data acquisition method based on artificial intelligence |
| CN115294515A (en) * | 2022-07-05 | 2022-11-04 | 南京邮电大学 | Artificial intelligence-based comprehensive anti-theft management method and system |
| CN116740813A (en) * | 2023-06-20 | 2023-09-12 | 深圳市视壮科技有限公司 | Analysis system and method based on AI image recognition behavior monitoring |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113315649A (en) * | 2021-04-21 | 2021-08-27 | 重庆科创职业学院 | Communication data acquisition method based on artificial intelligence |
| CN115294515A (en) * | 2022-07-05 | 2022-11-04 | 南京邮电大学 | Artificial intelligence-based comprehensive anti-theft management method and system |
| CN116740813A (en) * | 2023-06-20 | 2023-09-12 | 深圳市视壮科技有限公司 | Analysis system and method based on AI image recognition behavior monitoring |
| CN116740813B (en) * | 2023-06-20 | 2024-01-05 | 深圳市视壮科技有限公司 | Analysis system and method based on AI image recognition behavior monitoring |
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