International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Volume4,August 2017,

Topic : Abandoned Object Detection Based on Statistics for Labeled Regions

Authors:Miss.Snehal B. Dahivelkar || Prof. S. B. Borse

Abstract:Many public or open areas are facilitated with cameras at multiple angles to monitor the security of that area for keeping citizens safe. This is known as the surveillance system. Terrorism & global security are one of the major issues worlds facing now days. Abandoned object detection is an essential requirement in many video surveillance contexts. terrorist attacks involving some suspicious bags, box, or any other thing which are left unattended at railway stations, shopping malls, airports or any other public venue, with the rising concern about the security in public places abandoned object detection become very useful system to detect and recognize the suspicious activities that might dangerous to public safety. The most challenging task in video surveillance system is to detect such kind of suspicious bags. So, for that purpose it is necessary to have an efficient threat detection system which can detect & recognize strongly dangerous situations. Many methods were employed for detection of unattended objects but in this paper our focus is on the detection of abandoned objects in video surveillance system. The goal of this project is to design and implement a system which will be able to detect abandoned luggage using the captured videos from the camera as the input of the system. The system realizes image segmentation and image tracking, creates blobs of Objects, labels the blobs based on the shape and size of binary blobs and accordingly the status of baggage is defined in order to take appropriate action. The complexity of the problem arises from obstructions present in scene, lightening conditions & shadows. Our system is able to successfully overcome these difficulties to obtain impressive results..

Keywords: visual surveillance, abandoned object detection, image segmentation, image tracking, blobs of Objects.

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DOI: 01.1617/vol4/iss8/pid84071

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Dr.Zhongfu Tan
National Professor,
North China Electric Power University,

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ISSN(Online): 2394-6849

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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Journal Code : IJERECE
Electronic ISSN : 2394-6849
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Frequency : monthly
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