Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique

Abstract

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.

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

“…However, the R-CNN consumed more time and required large resources for object segmentation, which degraded the detection performance in certain cases. Sathesh [21] presented the image-processing technique for detecting abandoned things in airports. The input video undergoes pre-processing, foreground, and background distinction using Kalman Filter (KF), and object recognition using a faster Region-based CNN (R-CNN).…”

“…• The co-locating of abandoned objects is difficult among densely populated areas, such as railway stations, airports, etc [4]. • The tracking of abandoned baggage and individual behavior was not detected in time series in [21]. Thus, the respective person co-located with the object was not recognized.…”

Abandoned Object Detection and Classification Using Deep Embedded Vision

“…Object segmentation and static object analysis involve the identification, tracking, and assessment of an object's presence [12]. The most recent advancement in video surveillance technology allows it to automatically identify abandoned objects in public areas and illegally parked cars in traffic monitoring systems [13]. Detection of an abandoned object in video surveillance is challenging and essentials for maintaining safety [14].…”