MARC details
| 000 -LEADER |
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| fixed length control field |
02941ntm a2200289 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20231021150021.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230808s2022 |||a|||| bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
TUPM |
| Language of cataloging |
eng |
| Transcribing agency |
TUPM |
| Description conventions |
rda |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
BTH QA 76 |
| Item number |
A48 2022 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Altar, Christian Paul R. |
| 245 10 - TITLE STATEMENT |
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| Title |
Development of an anomaly recognition system for video surveillance systems inside an educational institution using deep learning algorithm / |
| Statement of responsibility, etc. |
Christian Paul R. Altar, Christian Pol C. Macuja, Adriane Vergel C. Riola, Lorenzo John D. Semilla. |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Manila : |
| Name of producer, publisher, distributor, manufacturer |
Technological University of the Philippines, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2022. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 94 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
28 cm. + |
| Accompanying material |
1 CD-ROM (4 3/4 in.) |
| 500 ## - GENERAL NOTE |
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| General note |
Thesis (Undergraduate) |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
College of Science -- |
| Degree type |
Bachelor of Science in Computer Science, |
| Name of granting institution |
Technological University of the Philippines, |
| Year degree granted |
2022. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references. |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Surveillance cameras are being set up in establishments, one is in educational institutions, to monitor areas from any abnormal events that might happen at the said location. However, these abnormal events need to be reviewed manually by personnel to determine if an abnormal event has happened, thus spending more time in viewing a lengthy video. An Anomaly Detection System, that will be developed through Python and with Deep Learning algorithm, aids in detecting abnormal events in videos that the surveillance system has recorded. Python is used in making the system for usable modules in video and image manipulation like OpenCV, Keras, and TensorFlow. Python also provides modules for data treatment like NumPy and Scikit. With these, training the system can be made more accurate with higher computer specifications, specifically in CPU, Graphics Processing Unit, and Random Access Memory usage. This system enhances security measures in a way that this will make anomaly detection faster for videos recorded by surveillance systems. A spatiotemporal model consisting of convolutional layers and convolutional LSTM layers can showcase a considerably good score in correctly predicting normal events from anomalous events. Alongside this, the system yielded an accuracy of at most 75% in correctly predicting abnormal events and normal events from test dataset. Furthermore, the results revealed that the system is highly rated in usability as the mean rating is 3.7 which is considered highly acceptable, and the lowest rate the system obtained is 3.52. A system deemed as highly acceptable in usability can achieve specified goals with efficiency in a designated utilization. --Author's Abstract |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Video surveillance. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Deep learning (Machine learning). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Anomaly detection (Computer security). |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Macuja, Christian Pol C. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Riola, Adriane Vergel C. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Semilla, Lorenzo John D. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Bachelor's Thesis COS |
| Suppress in OPAC |
No |