ATM crime detection using image processing integrated video surveillance: a systematic review

Sikandar, Tasriva; Ghazali, Kamarul Hawari; Rabbi, Mohammad Fazle

doi:10.1007/s00530-018-0599-4

Cited by 26 publications

(11 citation statements)

References 74 publications

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“…Although there are few well-known face image database existed and have been used in the previous studies conducting research on covered face detection, those database are not compatible with ATM environment. In addition, the face image database from ATM camera is not easy to access due to the security of bank [11]. Therefore, in this study we opt to create our own dataset considering the indoor ATM environment in university premises.…”

Section: Experiments Design and Datasetmentioning

confidence: 99%

“…Although a large number of studies have been found on application of covered face detection methods in ATM surveillance, most of the methods lacked to consider real ATM surveillance captured scene (camera view, camera-user distance) and face covering accessories. A detailed review of the previous studies on image processing integrated ATM video surveillance has been discussed in our systematic review in [11]. Previous methods were found employing variety of trained detectors, classifiers, complex functions and highly iterative algorithms such as Gabor wavelets, principal component analysis (PCA), nearest neighbor (NN), Chi-square distance and ellipse fitting.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Method for Detecting Covered Face Scenarios in ATM Surveillance Camera

et al. 2020

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Covering face with accessories such as mask, scarf and sunglass is a common criminal activity in automated teller machine (ATM) robbery. Therefore, detection of covered face using ATM surveillance camera can be an effective solution to reduce robbery or crime. This paper presents a novel method to detect covered face from ATM surveillance camera images. Specifically, three facial features, i.e., skin color, elliptical face shape and facial width-to-height ratio (fWHR), incorporated with geometrical property of ellipse have been employed to estimate the covered region. In addition, three parameters, i.e., facial area, fWHR and covered area percentage, have been utilized for reliable classification. Experiment results demonstrate that the method can detect full covered, uncovered and partially covered faces at a correct detection rate of 98.3%, 93.3% and 97.78%, respectively. The overall correct detection rate is 96.48%, which is found to be better than previous studies. Also, the proposed method can handle faces covered with few new face hiding objects such as hijab, niqab and robber's ski mask. Furthermore, processing time of the proposed algorithm is significantly improved while it is compared to the existing methods. The detection time varies between 31 and 67 ms which is equivalent to 15-32 frames per second.

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Section: Experiments Design and Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Detecting Covered Face Scenarios in ATM Surveillance Camera

et al. 2020

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“…However, there is no implementation process mentioned in this article. [15] According to the statistics and ability to get good accuracy and performance authors chose armed and partially covered face detection as the core component of the system…”

Section: Literature Reviewmentioning

confidence: 99%

Armed and Partially Covered Face Related Robberies Alerting System Using Computer Vision

Mahima¹,

Ginige²

2020

AJCT

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Robbery is the completed or attempted theft, directly from a person, of property or cash by force or threat of force, with or without a weapon, and with or without injury. Armed robbery is a serious crime that can traumatically that emotionally and mentally profoundly traumatize its victims. Armed robbery is usually motivated by a desire to acquire money, which is then commonly used to buy drugs. [1] However, some armed robbers are associated with the crime.[2] In the current decade, armed robbery is one of the major issues in society. According to Statista research department, there were more than 0.25 million armed robberies happened in the USA in 2018. [3] Moreover, these robberies accounted for an estimated $438 million in losses. [2] . Moreover is the USA there were more than 10000 murders, victims, by weapons in 2018. [4]When analyzing most of these armed robberies have happened locations are Banks, Gas or Service stations, and commercial houses in the USA. To monitor and act accordingly to the issue still, there is no proper method. Present in most places there are several security officers to monitor these robberies within 24H using CCTV cameras. In this research, the authors propose a novel approach to prevent this issue using a computer vision-based armed robberies alerting system. Here this system is able to detect the weapons of the robbers. Since most of the time robbers come with partially covered faces. In this research, the proposed system is able to detect partially covered faces also. When the system identified weapons or a partially covered face in a bank or a commercial house, it will send an alert by notifying the risk of the robbery to the in house security officers and relevant authorities. The proposed solution used an object detection model and a facial landmark identification based approach to detect robbers. Because of this system, no longer the security officers need to monitor CCTV cameras by themselves Keywords -

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“…The first type of works (Visual Surveillance of moving object and behavior) is to detect, identify, track specific objects from the sequences of frame image, and to understand and explain the object behaviors. There are a number of publications and researches in the visual surveillance field dealing with implementation of algorithms to perform traditional techniques of image processing such as motion detection [24], [26], [57], [58], object detection [17], [25], [31], [57], [59]- [71], [99], object or event classification [16], [20], [34], [35], [54], [66], [144], [145], [149]- [158], object tracking [2], [7], [15], [32], [35], [56], [70], [98], [99], [134], [136], [139], [159]- [168], behavior and activity recognition [60], [101], [102], [111], [134], [137] and database or system operations [153], [169]- [172]. The aim of the researches is to build intelligent...…”

Section: Research Topic In the Intelligence Video Surveillancementioning

confidence: 99%

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Shidik¹,

Noersasongko²,

Nugraha³

et al. 2019

IEEE Access

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Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.INDEX TERMS Artificial intelligence, cloud video surveillance, intelligent video surveillance, video surveillance framework.This study is conducted as follows: the methodology of the study is presented in Section 2. The outcomes and answers to the research questions are then discussed in Section 3. Finally, the study is summarized in the last section.

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ATM crime detection using image processing integrated video surveillance: a systematic review

Cited by 26 publications

References 74 publications

An Efficient Method for Detecting Covered Face Scenarios in ATM Surveillance Camera

An Efficient Method for Detecting Covered Face Scenarios in ATM Surveillance Camera

Armed and Partially Covered Face Related Robberies Alerting System Using Computer Vision

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

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