Remote classroom action recognition based on improved neural network and face recognition

2021 ◽  
pp. 1-11
Author(s):  
Lijun Mao
Keyword(s):  
Artificial Intelligence ◽  
Action Recognition ◽  
Computer Technology ◽  
Classroom Behavior ◽  
Recognition Algorithm ◽  
Video Tracking ◽  
Educational Video ◽  
Video System ◽  
Broadcasting System ◽  
Artificial Intelligence Technology

In recent years, the field of computer vision is promoted by the development of intelligent technology and computer technology, and has made breakthrough progress. Intelligent hardware technology and computer technology lay the foundation for the development of computer vision field. At the same time, the continuous improvement and development of artificial intelligence technology has also promoted the rapid development of educational video system, and the video tracking of educational video system has made breakthrough progress. By fully using intelligent hardware and computer technology, and combining with artificial intelligence technology, the video tracking and recognition technology of educational video system has been further developed, and new recognition algorithm has been adopted. The accuracy of tracking recognition is greatly improved, which can accurately identify the action of the characters. At the same time, through the use of new action recognition algorithm, not only improve the accuracy of educational video recognition, but also improve the speed of recognition, which can accurately capture the changes of people’s behavior in the classroom. The time consumed by the action recognition algorithm is very short, and the speed of the algorithm is very high. This new algorithm greatly improves the efficiency of the education recording and broadcasting system, and improves the accuracy and accuracy of the education recording and broadcasting system. This paper studies a set of intelligent image recognition system for students’ classroom behavior. It compiles and explains the intelligent system software systematically. The operation of this system is no single. It operates through the joint operation of many modules. It can realize online distributed homework, accurately and quickly identify students’ classroom behavior, and can also help students to identify their classroom behavior accurately and quickly. The classroom behavior of the accurate analysis of students’ incorrect classroom behavior to make timely reminders, greatly improve the efficiency of the classroom, improve the degree of concentration of students. In this paper, many classroom behaviors are simulated, and the performance of this software platform is predicted through many experiments.

The Designment of Digital Greenhouse Expert System

2012 ◽  
Vol 190-191 ◽  
pp. 199-204
Author(s):  
Jun Sun ◽  
Feng Zhang ◽  
Zhuang Chen
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Pest Control ◽  
Computer Technology ◽  
Basic Information ◽  
Crop Cultivation ◽  
Digital Management ◽  
Artificial Intelligence Technology ◽  
Management Techniques ◽  
Healthy Growth

In order to improve the management of crops in digital greenhouse, and give crops a healthy growth, the digital greenhouse expert system is developed based on the growth knowledge of agricultural greenhouse, computer technology and artificial intelligence technology. The expert system can provide basic information about crop growth, cultivation and management techniques, pest control and other diagnostic functions, as well as expert online answering question and so on. The development of the system is advantageous to standardize digital management of greenhouse crop cultivation, reduce the burden of planting staff, and it also can improve crop quality.

scholarly journals An Attention Enhanced Spatial–Temporal Graph Convolutional LSTM Network for Action Recognition in Karate

2021 ◽  
Vol 11 (18) ◽  
pp. 8641
Author(s):  
Jianping Guo ◽  
Hong Liu ◽  
Xi Li ◽  
Dahong Xu ◽  
Yihan Zhang
Keyword(s):  
Artificial Intelligence ◽  
Action Recognition ◽  
Structural Information ◽  
Human Action Recognition ◽  
Human Action ◽  
Competitive Sports ◽  
Convolutional Networks ◽  
Convolution Model ◽  
Artificial Intelligence Technology ◽  
Temporal Graph

With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition technology, based on deep learning, has gradually been applied to the analysis of the technical actions of competitive sports athletes, as well as the analysis of tactics. In this paper, a new graph convolution model is proposed. Delaunay’s partitioning algorithm was used to construct a new spatiotemporal topology which can effectively obtain the structural information and spatiotemporal features of athletes’ technical actions. At the same time, the attention mechanism was integrated into the model, and different weight coefficients were assigned to the joints, which significantly improved the accuracy of technical action recognition. First, a comparison between the current state-of-the-art methods was undertaken using the general datasets of Kinect and NTU-RGB + D. The performance of the new algorithm model was slightly improved in comparison to the general dataset. Then, the performance of our algorithm was compared with spatial temporal graph convolutional networks (ST-GCN) for the karate technique action dataset. We found that the accuracy of our algorithm was significantly improved.

Utilization of Artificial Intelligence Technology in Assisting House Chores

2021 ◽  
Vol 2 (1) ◽  
pp. 29-34
Author(s):  
Sakkiz Nhizam ◽  
Muazzem Zyarif ◽  
Sarhad Ziyyo Tuhfa
Keyword(s):  
Artificial Intelligence ◽  
Human Capital ◽  
Computer Technology ◽  
Consumer Electronics ◽  
Household Appliances ◽  
Intelligent Algorithms ◽  
Artificial Intelligence Technology ◽  
Repetitive Analysis

AI is accomplished by the combination of a large amount of input, repetitive analysis, and intelligent algorithms. This enables the program to automatically learn from the trends or features found in the results. Artificial intelligence is also being used in a variety of areas, one of which is schooling. It may also be used to assist households with domestic chores. Technology was developed to improve the life of a large number of citizens. Western technology is heavily reliant on computer technology as a result of a scarcity of human capital. That is why they developed a large number of robotic machines. Technology is extremely beneficial in terms of performance, efficacy, and also cost; utilizing technology is significantly less expensive. Any of the most prominent AI developments are reimagining the consumer electronics market, such as the smarthome. AI has enabled the easy control of household appliances.

Research on the framework of intelligent classroom based on artificial intelligence

2021 ◽  
pp. 002072092098400
Author(s):  
Jun Li ◽  
Xu Tan ◽  
Yanhui Hu
Keyword(s):  
Artificial Intelligence ◽  
System Architecture ◽  
Evaluation Index ◽  
Knowledge Map ◽  
Educational Video ◽  
Education Equity ◽  
Artificial Intelligence Technology ◽  
Deep Integration ◽  
Intelligent Classroom ◽  
Important Form

The emergence of artificial intelligence has greatly improved the imbalance of resource allocation in different regions of China, and promoted education equity. Video is an important form of learning resources in the field of education. Educational video analysis and innovative application based on artificial intelligence technology greatly promote the innovation of education and teaching, and promote the deep integration of artificial intelligence and education. In this paper, firstly, the system architecture is introduced, secondly, the model of education knowledge map which can establish the mapping relationship among knowledge, problem and ability is proposed, and finally, the evaluation index of intelligent classroom is designed. Intelligent classroom based on artificial intelligence technology is helpful to enhance interactive learning and promote the construction of intelligent campus.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

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