Description:Field of the Invention
The recommended invention is a contemporary AI-powered student-monitoring system that will change behavior analysis, attendance tracking, and event detection in educational institutions. This full system captures events such as dozing in class, phone use, conflicts, and cases of college property damage automatically. The system offers automatic attendance management, which supports a pleasant learning environment. Its adaptability implies that it adjusts to shifting educational needs, redefining behavior monitoring and event detection in academics.
Objective of the Invention
The AI-Powered Student Tracking System's principal purpose is to revolutionize student behavior analysis, attendance management, and incident detection in educational institutions. The system seeks to give real-time information about behaviors such as dozing in class, phone use, and arguments by utilizing AI algorithms and hardware integration. At the same time, it streamlines attendance management via automated procedures. The invention also intends to enhance campus safety by immediately recognizing situations such as property damage and giving out real-time warnings. This multimodal method offers educators effective intervention tools, reacts to changing educational needs, and produces a good learning environment that encourages academic success as well as personal development.
Background of the Invention
As online education has begun, it has been challenging to establish that learning environment. Students have become irresponsible, and many use mobile phones and sleep during class hours. In addition, there are misbehavior activities that are taking place, groups of students are gathering and fighting, making it difficult to track their performance and behavior in their classes.
For instance, CN109636688A outlines "Students Behavior Analysis System Based on Big Data" designed to analyze and predict students' behavior in an educational context. It utilizes data from various sources such as video monitoring, and more. The system processes and optimizes the collected data categorizes it using clustering algorithms and then conducts multi-dimensional analysis to predict student behaviors related to attendance, consumption, movement, and online activities. The goal is to provide insights into students' preferences, physical conditions, economic situations, and more, enabling personalized guidance, early warnings, and intervention strategies for educators, administrators, and parents to support students effectively.
CN110991381B presents a real-time classroom student status analysis and indication reminding system and method based on intelligent recognition of behaviors and voices. The system employs multi-modal information sensing and collection modules, intelligent data recognition, classroom context knowledge base, data analysis, and status reporting modules. It detects students' behaviors and voice interactions, analyzes their learning states, and provides real-time indications and adjustments for both students and teachers. The method involves collecting student images and voice data, processing and recognizing behaviors and interactions, determining learning states, indicating student conditions, and allowing for manual or automatic state adjustments. This technology aims to enhance classroom learning by providing insights into student engagement and enabling timely interventions.
CN110992741B pertains to a learning assistance method and system centered on analyzing classroom emotions and behaviors. The method involves collecting image and voice data from students and teachers, processing the image data to recognize facial features and calculate similarity with stored information, analyzing expressions and postures, evaluating students' engagement during lectures, and determining their absorption of knowledge points explained by the teacher. The system comprises data acquisition, student knowledge analysis, teacher content analysis, and learning push modules. The system aims to enhance students' learning by offering exercises tailored to their engagement and understanding levels. The invention emphasizes combining real-time behavior analysis with teaching content to provide effective learning support in a classroom setting.
CN111160277A presents an innovative behavior recognition analysis method and system, primarily applicable to classroom settings. It involves converting video data into frame images and extracting individual behavior information using a combination of algorithms, including Yolov3 for detection and Hourglass for skeleton data. A graph convolutional network (GCN) is employed to classify behaviors and interactions among students. The system monitors engagement levels and, if participation falls below a threshold, sends tailored prompt messages to teachers and parents, facilitating personalized intervention strategies. The invention enhances teaching quality by analyzing behavior patterns and engagement in a classroom environment, aiding teachers and parents in optimizing student learning experiences.
Similarly, US20210407103A1 disclosed patent describes an object tracking method for electronic devices, particularly cameras. The method involves obtaining and decoding a video stream to acquire image frames. It classifies frames as first-type (for detection) or second-type (for tracking). For first-type frames, object detection and subsequent key point detection are used to determine the target object's position, predicting its next position based on key point results and motion vectors. In second-type frames, the method predicts the next position using motion vectors, motion vector biases, and region translation. The method also handles cases with multiple target objects and pedestrian tracking. An electronic device executes the method, allowing real-time tracking with improved efficiency and accuracy.
Summary of the Invention
The presented invention is an AI-based student tracking system designed to revolutionize behavior analysis and incident detection within educational institutions. The system employs advanced AI algorithms and hardware integration to capture and analyze student behaviors such as sleeping in class, phone usage, conflicts, and property damage. The main objective of the system is to provide real-time insights into student behavior.
The student tracking system employs cutting-edge technologies such as AIML to track his/her behavior. The core concept of the invention revolves around the integration of AI algorithms, particularly utilizing open CV and object detection with the assistance of Convolutional Neural Networks (CNN), It consists of a camera through which the person's image is detected using an OpenCV module, and it is then passed to the CNN model, which is trained based on the input given (misbehavior activities, sleeping, eating, damaging college property), and the model employs YOLO (You Only Look Once V8) to accurately predict the calculations this model provides.
In summary, the AI-powered student tracking system introduces a comprehensive solution to transform behavior analysis, and incident detection in educational institutions. By leveraging AI, hardware integration, and real-time insights, the system aims to enhance student engagement, safety, and overall learning experiences.
Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1: Flowgorithm representing the workflow of the student behaviour analysis system.
Figure-2: Diagrammatic representation of the implementation of CNN
Figure-3: Flow chart representing the basic architecture and workflow of the developed prototype
Detailed Description of the Invention
The detailed description of the AI-powered student tracking system is as follows:
The new student tracking system uses smart technology to help schools deal with issues like how students behave, attendance, and incidents. As schools change with online learning and different student behaviors, this system uses clever computer programs and special equipment to create a better learning and environment. It helps teachers understand how students behave, keeps track of who's in class, and notices if something goes wrong. This system brings all these important things together to make schools a better place for learning.
At the heart of the system's functioning lies its advanced use of AI technologies like open CV and object detection methods facilitated by Convolution Neural Networks (CNNs). This intricate combination empowers the system to independently recognize various student behaviors during classes, such as students sleeping, using phones, and causing disruptions. By relying on visual cues captured by strategically placed cameras, the AI model becomes skilled at identifying and categorizing these behaviors in real-time.
An outstanding feature of the system is its exceptional ability to quickly identify and report incidents, especially cases of damage to college property. This capability comes from the AI-driven analysis, allowing it to promptly distinguish between accidental and deliberate property damage. Early detection enables swift actions, preventing further harm and holding accountable those responsible. Real-time alerts sent to administrators and relevant staff ensure quick interventions and effective control of damage.
The user interface of the system, designed with user-friendliness in mind, acts as a central hub for educators, administrators, and authorized personnel. This comprehensive dashboard consolidates various data points, providing real-time insights into student behaviors, attendance records, and documented incidents. It's versatile enough to let users explore historical data and generate comprehensive reports, empowering informed decision-making and strategies for further development.
What sets this system apart is its adaptability, driven by continuous improvement through data analysis and user feedback. This ongoing process refines its AI algorithms over time, allowing the system to predict and respond effectively to changing student behavior patterns. This inherent flexibility ensures that the system stays capable of addressing the ever-changing education landscape, adapting to evolving needs and the dynamic nature of student behaviors.
In summary, the AI-powered student tracking system introduces an innovative solution set to transform behavior analysis and incident detection in education. By merging AI capabilities and hardware integration, the system aims to foster an environment that encourages engagement, efficient incident control, and an overall enriched learning experience.
Advantages of the proposed model,
The new AI-powered student tracking system comes with several advantages that make it a game-changer in how schools manage behavior and incidents. One big benefit is how it watches and understands how students behave. Using smart technology, it can tell if students are sleeping in class, using phones, or doing things that might disrupt learning. This helps teachers and school staff better understand what's happening in the classroom and how to help students learn better.
Another great thing about the system is that it's like a superhero for keeping the school safe. It can quickly spot if something gets broken or damaged in the school. This helps stop small problems from becoming big ones, and it encourages everyone to take care of school property.
The system also makes keeping track of attendance super easy. It uses special cameras to automatically see who's in the classroom, so teachers don't have to call roll and mark attendance on paper. This saves time and makes sure everyone's attendance is correct.
For teachers and school leaders, the system's dashboard is like a control center. It shows important information all in one place, like how students are behaving, who's present, and any incidents that have happened. This makes it much easier to make decisions and create plans to improve the school environment.
In short, the AI-powered student tracking system brings a bunch of good things to schools. It helps teachers with behavior and attendance, keeps the school safer, and gives everyone the tools to make smart choices based on what's happening in the school.
6 Claims and 3 Figures , Claims:The scope of the invention is defined by the following claims:
Claims:
1. The tracking system to analyse the students behavior comprises:
a) The model effectively identifies uniform compliance issues, including unauthorized clothing like, t-shirts, and improper footwear. This ensures a consistent and disciplined dress code among students.
b) The model identifies sharp objects, knives, or potentially hazardous items, enhancing campus safety by promptly recognizing and addressing potential threats.
c) The model surveils hostel verandas, detecting unusual activities like unauthorized sports or conflicts, and creating a secure and harmonious living environment for hostel residents.
2. As per claim 1, the electricity consumption is optimized by monitoring lights and fans after college hours or during classroom vacancies. This leads to energy conservation and cost savings by preventing unnecessary electricity use.
3. T As per claim 1, the system detects misbehavior activities in real-time, such as eating during class, sleeping, and using mobile devices. This encourages improved classroom behavior and engagement, enhancing overall learning experiences.
4. As per claim 1, the system controls student movement by tracking and preventing roaming in corridors during class and off-hours, contributing to an organized and focused campus environment.
5. As per claim 1, the system manages attendance accurately, tracking students present for at least 80% of class time. This offers precise attendance records for improved attendance management.
6. As per claim 1, The system reduces food wastage by discouraging improper disposal of food, promoting responsible behavior and resource conservation.