Moving Object Detection Based on Improved ViBe Algorithm

Gu, Bo; Song, Kefeng; Dao-yin, Qiu; Zhang, Hongtao

doi:10.14257/ijsh.2015.9.12.23

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…The statistical background model also provides the mechanism which allows the system to adapt to minor changes made in the background. On the other hand, the use of periodical adaptation is employed when there is a need to synchronize lighting with other physical changes that may be made to the background such as the removal or addition of objects in the system [15,16]. Secondly, Background Subtraction: method of adaptive background subtraction refers to the approach that is employed to detect the foreground objects from the n image.…”

Section: Pre-processingmentioning

confidence: 99%

Automated Cheating Detection based on Video Surveillance in the Examination Classes

Al_airaji

Aljazaery

Alrikabi

et al. 2022

Int. J. Interact. Mob. Technol.

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A major issue in the field of education is exam malpractice and other forms of academic dishonesty, where efforts made towards the assessment of students are undercut. In order to address this menace, a cheating detection system has been proposed in this study. The system can detect cheating in real-time. The proposed system uses video surveillance to monitor the activities of the student during exams, especially abnormal behavior. The development of the system involved the use of three different techniques, with different functions. The first technique helps in determining the direction of the students’ heads when they move from their initial direction, which is the exam script. Some form of cheating involves students peeping at the exam scripts of other people writing the same exam. Whenever the system observes a deviation that exceeds the set threshold, it classifies the behavior as abnormal. The second technique works with the movement of the student’s iris. It detects when a student’s iris moves in a different direction to copy answers from written documents like mobile phones, books, hands, summary papers. The third technique is used in identifying the contact between a student’s hands and face, as well as that between different students for shared abnormal behavior detection between students, such as sharing of incriminating materials. When any of these is detected, an automatic alarm alerts the authority about the abnormal behavior that has been detected, thereby minimizing the error rate that can occur as a result of manual monitoring.

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Section: Pre-processingmentioning

confidence: 99%

Automated Cheating Detection based on Video Surveillance in the Examination Classes

Al_airaji

Aljazaery

Alrikabi

et al. 2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…In order to remove the ghost area in the process of foreground detection, different modified versions of ViBe algorithm have been introduced by other authors. For instance, Bo et al 44 improved ViBe algorithm based on the theory that the histogram distribution characteristics of moving objects are different when a real object is moving. However, the histogram of ghost areas has a correspondence distribution characteristic.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand the original ViBe algorithm could easily produce ghost in the process of moving object detection. 44 Ghost described in Cucchiara et al 45 as "a set of connected points detected as in motion by means of background subtraction, but not corresponding to any real moving object. "…”

Section: Related Workmentioning

confidence: 99%

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Dorudian

Lauria

Swift

2019

International Journal of Micro Air Vehicles

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A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

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Coarse-to-fine sample-based background subtraction for moving object detection

Zhang

2020

Optik

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Moving Object Detection Based on Improved ViBe Algorithm

Cited by 6 publications

References 12 publications

Automated Cheating Detection based on Video Surveillance in the Examination Classes

Automated Cheating Detection based on Video Surveillance in the Examination Classes

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Coarse-to-fine sample-based background subtraction for moving object detection

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