Date of Publication

2022

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science Major in Computer Systems Engineering

Subject Categories

Computer Sciences | Systems Engineering

College

College of Computer Studies

Department/Unit

Computer Technology

Thesis Advisor

Macario O. Cordel, II

Defense Panel Chair

Joel P. Ilao

Defense Panel Member

Judith J. Azcarraga
Ronald M.Pascual

Abstract/Summary

There are many quality datasets that are being used in anomalous detection systems. However, with the range of possible machine learning approaches, each of which has specific input annotation requirements e.g. levels of annotation whether frame level or object level, types of input whether image or video, types of annotation whether fixation data or object location, types of anomalous events etc., the development of the anomalous event detection system is limited by the type available annotated dataset. Although video annotation tools exist to fill these gaps, they are mostly tailored towards object detection systems. This study aims to develop an annotation tool specifically for labeling anomalous road events that can aid in creating datasets for training machine learning models. The proposed system patterned on existing video annotation systems which are used to annotate objects and events inside a video. There are two features that differentiate it from non-anomaly annotation systems. The first feature is a dedicated post processing module, which is used to package the final output towards anomaly detection. The second feature is an automation module which is used to automate the annotation process specifically for annotating anomalous events.

Abstract Format

html

Language

English

Format

Electronic

Physical Description

[155 leaves]

Keywords

Anomaly detection (Computer security)

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Embargo Period

7-6-2022

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