Vision-based ID wearing detection system using IP camera

Date of Publication

2016

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Electronics Engineering

Subject Categories

Electrical and Electronics | Systems and Communications

College

Gokongwei College of Engineering

Department/Unit

Electronics and Communications Engineering

Thesis Adviser

Melvin K. Cabatuan

Defense Panel Chair

Alexander C. Abad

Defense Panel Member

Roy Francis R. Navea
Mark Lorenze D. Torregoza

Abstract/Summary

For security purposes, many institutions adapt an ID wearing policy. In the case of academic institutions, the number of discipline officers is less than the number of students. With this, instances of not wearing an ID card might be inevitable. This study aims to develop a vision-based ID wearing detection system using IP cameras. This is one of the methods to check and monitor students whether or not they are wearing their IDs while they are in the campus. This study is significant in aiding the Discipline Officers in the case of DLSU-STC in monitoring students at different locations like the Ground Floor East Entrance, the Ground Floor East Canopy and the 2nd Floor Lobby of Milagros R. Del Rosario Building. The system uses one internet protocol (IP) camera implement a motion detection algorithm for detecting the moving objects, Linear Binary Patterns (LBP) for face detection and object (ID card) detection, and object annotation procedures to gather training data sets. The researchers considered two approaches: Contour using Motion Detection and Contour using Region of Interest.

Results show that the first approach, the Contour using Motion Detection algorithm, has a better result accuracy compared to the second approach, the Contour using Region of Interest, since the first approach looks for the ID card if there is a detected motion while the second approach looked for the ID when the contour is formed. Accuracies were calculated per location resulting to a range between 93% to 96%.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU17031

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

xii, 164 leaves, color illustrations, 28 cm. + 1 computer disc (4 3/4 in.)

Keywords

Image processing—Digital techniques; Identification—Equipment and supplies

Embargo Period

5-4-2021

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