A New Approach to Detect Anomalous Behaviour in ATMs

Parab, Aditya; Nikam, Abhishek; Mogaveera, Prajwal; Save, Ashwini

doi:10.1109/icaccs48705.2020.9074417

Cited by 16 publications

(6 citation statements)

References 10 publications

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“…In 2020, Parab, A., et al [21] suggested a DL method for analysing ATM surveillance footage to nd anomalies. The suggested system will be able to determine whether or not there is an irregularity in the ATM.…”

Section: Literature Surveymentioning

confidence: 99%

YOLO ROBBERY: Video Surveillance based Robbery Prediction and Alert System Using YOLO V7

Chidambaram,

Chandrasekaran

2023

Preprint

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Detection of anonymous behavior is a method of detecting the behavior of people who are insignificant. By using video surveillance and anomaly detection, it is possible to automatically see when something that does not fit the usual pattern is captured by the camera. Although it is a challenging task, it is crucial to automate, improve, and lower expenses in order to detect crimes and other calamities. In this paper, a novel YOLO-Robbery network has been introduced for enhance the security by identifying the threat activities in the supermarket and send the alert message to the shop owner automatically. Initially, the surveillance camera's real-time footage is collected and transformed into image frames for subsequent processing. These frames are pre-processed using multi-scale retinex to remove distortions and augmented to increase the data frames. This work utilizes the YOLO V7 network to extract features from surveillance camera images to quite effective at recognizing and classifying threats at supermarket. Finally, Greedy snake optimization is used to fine-tune the hyperparameters of YOLO V7 network it is trained using DCSASS dataset for efficient image recognition and the alert message is sent to the shop owner automatically. The proposed method has been simulated using MATLAB. The experimental result shows that the YOLO-Robbery method performance was evaluated using the DCSASS dataset in terms of accuracy, precision, recall, and specificity. The proposed YOLO-Robbery achieves the overall accuracy of 99.15%. The proposed YOLO-Robbery increases the overall accuracy range by 13.15%, 2.15%, and 6.24 better than CLSTM-NN, J. DCNN, and ANFIS respectively.

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Section: Literature Surveymentioning

confidence: 99%

YOLO ROBBERY: Video Surveillance based Robbery Prediction and Alert System Using YOLO V7

Chidambaram,

Chandrasekaran

2023

Preprint

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“…Their encoder efficiently encodes the changes in motion for detecting anomalies in a surveillance environment. Similarly, Parab et al [ 35 ] introduced a system based on a CNN and LSTM to detect unusual situations at an automated teller machine. In this model, the frame-level features are extracted from the videos and then fed to a bidirectional LSTM to classify abnormal events at an automated teller machine.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Ullah

Hussain

et al. 2021

Sensors

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Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

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“…A. Parab et al [2] proposed a new approach to detect anomalous behavior in ATMs. The authors focused on utilizing machine learning techniques for anomaly detection in ATM transactions.…”

Section: Literature Reviewmentioning

confidence: 99%

Anomalous Behavior Detection in ATM using Artificial Intelligence

Jain¹

2021

MSEA

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This paper proposes a new supervised algorithm for detecting abnormal events in confined areas such as ATM rooms and server rooms. The objective of this work is to establish a robust technical foundation that supports a secure and convenient social infrastructure, with abnormal behavior detection using image processing being one of the key technologies. Abnormal behavior detection involves creating a model based on normal behavior data and identifying any behavior that deviates from this model as abnormal. However, collecting comprehensive abnormal behavior data in advance can be challenging. Therefore, the ability to detect abnormalities using a model built solely on normal behavior data is highly valuable for practical implementation. This proposed work presents examples of abnormal behavior detection using image processing techniques applied to ATM surveillance videos. Additionally, typical examples of abnormal behavior detection through motion image processing are demonstrated. Furthermore, our approach enhances system security by verifying the identity of the cardholder during ATM transactions. The combination of image processing algorithms and supervised learning enables effective identification of abnormal events, contributing to a more secure and reliable social infrastructure.

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A New Approach to Detect Anomalous Behaviour in ATMs

Cited by 16 publications

References 10 publications

YOLO ROBBERY: Video Surveillance based Robbery Prediction and Alert System Using YOLO V7

YOLO ROBBERY: Video Surveillance based Robbery Prediction and Alert System Using YOLO V7

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Anomalous Behavior Detection in ATM using Artificial Intelligence

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