@@ -80,7 +80,7 @@ The main objectives of the research project could be further split into individu
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@@ -80,7 +80,7 @@ The main objectives of the research project could be further split into individu
## System Architecture
## System Architecture
![System Acrhitecture of SLPES](slpes_architecture.jpg)
![System Acrhitecture of SLPES](SLPES_System.jpg)
As mentioned in the given figure, this system will mainly consist of a mobile and web application. When the students and the lecturer enter the classroom, their behavior will be monitored by two separate video-cameras and a microphone. The collected data will transmit to the server through raspberry-pi and stores the gathered data in the MongoDB cloud database service. While the video camera tracks both facial expression and body movements, the microphone will record the lecture for further analysis. The student attendance will register by capturing facial identity using deep learning and computer vision mechanisms. Students will be monitored by capturing their facial and body movements by Computer Vision and Deep Learning algorithms. The lecturer audio and video details will be gathered and stored for analyzing as such lecture summarizing and monitoring lecturer performance using machine learning, deep learning, and computer vision technologies. While the students have access to the mobile application, the lecturers and the higher management have the authority to manage the web-application. Frontend components will communicate with the backend using Django REST API.
As mentioned in the given figure, this system will mainly consist of a mobile and web application. When the students and the lecturer enter the classroom, their behavior will be monitored by two separate video-cameras and a microphone. The collected data will transmit to the server through raspberry-pi and stores the gathered data in the MongoDB cloud database service. While the video camera tracks both facial expression and body movements, the microphone will record the lecture for further analysis. The student attendance will register by capturing facial identity using deep learning and computer vision mechanisms. Students will be monitored by capturing their facial and body movements by Computer Vision and Deep Learning algorithms. The lecturer audio and video details will be gathered and stored for analyzing as such lecture summarizing and monitoring lecturer performance using machine learning, deep learning, and computer vision technologies. While the students have access to the mobile application, the lecturers and the higher management have the authority to manage the web-application. Frontend components will communicate with the backend using Django REST API.