Human activity recognition and authentication system
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
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Brac University
2024
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| Online pristup: | http://hdl.handle.net/10361/22076 |
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10361-220762024-01-09T21:02:52Z Human activity recognition and authentication system ALam, Md. Zubaer Mostakim, Moin Department of Computer Science and Engineering, Brac University Human activity recognition Facial recognition Prediction Neural Network Pose estimation Machine learning Neural network--Computer science This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 15-19). Automated surveillance motion detection and video data analysis are now crucial jobs for many industries. Understanding human behavior from security video data is crucial, especially in places like banks, hospitals, superstores, and other restricted areas. The two most discussed subjects in the field of computer visions are face detection to identify people and human activity recognition. Over the past 20 years, numerous study projects have been conducted. I’ll discuss the Human activity Recognition and Authentication (HARAuth) System initiative in this essay. In this project, I’ll suggest an algorithm to identify human activity while also authenticating the individual to determine whether that person is authorized to perform that activity. In this work, I presented a method for classifying and recognizing particular activities based on the pose skeleton of a human. Pose estimation and classification are the first two steps in this procedure. This project uses the OpenPose library for its pose estimation tasks. Additionally, MLPClassifier from the Sklearn library is used to complete the activity classification job. I cropped each person’s rectangular area during the pose classification process based on the pose’s position in the frame-by-frame video image. Each person’s rectangular area is subjected to face recognition in order to verify their identity for the identified action. Md. Zubaer ALam M.Sc. in Computer Science 2024-01-09T04:16:31Z 2024-01-09T04:16:31Z 2023 2023-05 Thesis ID 20266033 http://hdl.handle.net/10361/22076 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 19 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Human activity recognition Facial recognition Prediction Neural Network Pose estimation Machine learning Neural network--Computer science |
| spellingShingle |
Human activity recognition Facial recognition Prediction Neural Network Pose estimation Machine learning Neural network--Computer science ALam, Md. Zubaer Human activity recognition and authentication system |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin ALam, Md. Zubaer |
| format |
Thesis |
| author |
ALam, Md. Zubaer |
| author_sort |
ALam, Md. Zubaer |
| title |
Human activity recognition and authentication system |
| title_short |
Human activity recognition and authentication system |
| title_full |
Human activity recognition and authentication system |
| title_fullStr |
Human activity recognition and authentication system |
| title_full_unstemmed |
Human activity recognition and authentication system |
| title_sort |
human activity recognition and authentication system |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22076 |
| work_keys_str_mv |
AT alammdzubaer humanactivityrecognitionandauthenticationsystem |
| _version_ |
1814307584970063872 |