Open AccessSpeedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Author(s) -
A. Sathesh,
Yasir Babiker Hamdan
Publication year - 2021
Publication title -
journal of trends in computer science and smart technology
Language(s) - English
Resource type - Journals
ISSN - 2582-4104
DOI - 10.36548/jtcsst.2021.4.001
Subject(s) - background subtraction , preprocessor , computer science , computer vision , artificial intelligence , identification (biology) , object (grammar) , set (abstract data type) , video tracking , image processing , object detection , tracking (education) , enhanced data rates for gsm evolution , image (mathematics) , pattern recognition (psychology) , pixel , psychology , pedagogy , botany , biology , programming language
Recently, in computer vision and video surveillance applications, moving object recognition and tracking have become more popular and are hard research issues. When an item is left unattended in a video surveillance system for an extended period of time, it is considered abandoned. Detecting abandoned or removed things from complex surveillance recordings is challenging owing to various variables, including occlusion, rapid illumination changes, and so forth. Background subtraction used in conjunction with object tracking are often used in an automated abandoned item identification system, to check for certain pre-set patterns of activity that occur when an item is abandoned. An upgraded form of image processing is used in the preprocessing stage to remove foreground items. In subsequent frames with extended duration periods, static items are recognized by utilizing the contour characteristics of foreground objects. The edge-based object identification approach is used to classify the identified static items into human and nonhuman things. An alert is activated at a specific distance from the item, depending on the analysis of the stationary object. There is evidence that the suggested system has a fast reaction time and is useful for monitoring in real time. The aim of this study is to discover abandoned items in public settings in a timely manner.