Human Tracking and Tagging

Project Title

Human Tracking and Tagging

Project Area of Specialization Artificial IntelligenceProject Summary

We live at a time when society is fast heading toward automation in all areas. Institutions, whether for surveillance or management purposes, require a system to track people within their organizations or workplaces. Our project's scope is focused on the following primary deliverables, but it is not restricted to these because the nature of the concept is scalable based on our progress:

• It will successfully track at least two personnel simultaneously in real-time using visual cues that could include facial recognition or unique identification numbers embedded in work attire etc.

• Upon successful recognition, relevant data will be retrieved from a database containing human records and personnel will be tagged with their specific information.

The system will accomplish its objectives by utilizing digital image processing techniques and then using convolutional neural networks to detect persons in video frames. For information retrieval, the system will additionally employ a bespoke database. A system like this may be used for a variety of things, including tracking staff in warehouses, mass surveillance by governments to increase security, and taking class attendance.

Project Objectives

We live at a time when society is fast heading toward automation in all areas. Institutions, whether for surveillance or management purposes, require a system to track people within their organizations or workplaces. Our project's scope is focused on the following primary deliverables, but it is not restricted to these because the nature of the concept is scalable based on our progress:

• It will successfully track at least two personnel simultaneously in real-time using visual cues that could include facial recognition or unique identification numbers embedded in work attire etc.

• Upon successful recognition, relevant data will be retrieved from a database containing human records and personnel will be tagged with their specific information.

The system will accomplish its objectives by utilizing digital image processing techniques and then using convolutional neural networks to detect persons in video frames. For information retrieval, the system will additionally employ a bespoke database. A system like this may be used for a variety of things, including tracking staff in warehouses, mass surveillance by governments to increase security, and taking class attendance.

Project Implementation Method

Methodology

Figure 10 - Model Snippet 1

Initially, we open the images folder, read all the images, store images in the "images" dictionary and then store the name of the image at the same index in the "className" dictionary. While storing the name of the image, we remove the extension and just store the name of the image.

Figure 11 – Model Snippet 2

We convert all the images from RGB to grayscale. Next, we detect the faces in each image and then find the encodings of the face and store them.

Figure 12 - Model Snippet 3

We open the camera and perform face detection in real-time. Once the face is detected, we find the encoding of the face. Using the encoding of the face, we compare it with all other stored face encodings. If there is an image that has a match of a distance lower than 0.39, then we label the detected face with the same label as the stored image and pass it to the checktimeandmarkAttendence function. If there is no image in the dataset which compares to a distance lower than 0.39, we label the image as "Unrecognized" and ignore it.

Figure 13 - Model Snippet 4

The checktimeandmarkAttendance function initially opens the file, which contains the scheduled start timings and dates of classes, and preprocesses them in a manner that is understandable by the computer.

Figure 14 - Model Snippet 5

We use the preprocessed times to see whether any class is currently in session. If there is a class in session and attendance needs to be taken, we print the class name and time in the CSV file and pass the name to the markAttendence function.

Figure 15 - Model Snippet 5

The mark attendance function will print the Sr no, Name, and entry time of the individual into the CSV file.

Figure 16 - CSV file

This is how the attendance CSV file looks like

Benefits of the Project

Motivation

The old technique for registering attendance - storing paper sheets for records, spending significant time manually confirming attendance, and generating reports with the potential for human mistakes - has become inefficient. Updates and maintenance of conventional attendance systems are likewise exceedingly time-consuming. It will take at least a few hours to update the entire school or college, regardless of whether the data is entered manually or digitally. This time may have been employed more effectively on other, more essential duties. Similarly, at the conclusion of the academic year, it would require an enormous amount of time to consolidate all of the records and generate individual student reports. Having an automatic attendance tracker eliminates this burden and compiles all records without requiring human interaction. Thus, the time saved can be allocated to more critical managerial tasks.

Project Perspective

The main purpose of this project is to provide end-users (instructors and administrators) with a platform that caters to their needs properly by automating the whole attendance-taking procedure due to which time and physical effort to monitor and track student attendance individually with integrity ensured. It is an end solution for the instructors and administration to monitor and review the attendance analytics of students. To schedule classes and be satisfied with a higher level of integrity in terms of the records. Absenteeism has long been the most significant issue in academic institutions. Students frequently drop classes for a variety of reasons. When a friend is given the job of standing in for an absent student during the roll call, it becomes difficult to keep track of their absences. In addition, many teachers frequently skip the roll call due to a lack of teaching time or laziness. Not only can this impair the academic achievement of the student, but it can also affect the professor's mood.

Technical Details of Final Deliverable

Admin log-on 

Store each student’s timestamp upon identification

Count the total number of students.

Video storage

Logout

Accuracy

Show attendance analytics on instructor’s portal

Detects in real-time

Final Deliverable of the Project Software SystemCore Industry EducationOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Quality EducationRequired Resources