Unusual event detection in low resolution video for enhancing ATM security

Goswami, Shambaditya; Goswami, Jyoti; kumar, N. Uday

doi:10.1109/spin.2015.7095435

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…LITERATURE REVIEW In this paper [1] Proposed algorithm only uses close morphological operation with disk like structuring element in the preprocessing steps to cope up with low resolution video. It further uses rolling average background subtraction technique to detect foreground object from dynamic background in a scene.…”

Section: IImentioning

confidence: 99%

Unusual Human Activity Recognition in ATM’S

2024

IJRASET

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This paper addresses the imperative need to enhance Automated Teller Machine (ATM) safetyand security through the integration of deep learning and OpenCV. Leveraging Convolutional Neural Networks (CNNs) for feature extraction and Siamese Networks for anomaly detection, our approach aims to fortify ATM systems against threats like skimming and unauthorized access. Realtime image processing with OpenCV facilitatesefficient preprocessing. The study critically examines existing methodologies, highlighting vulnerabilities to adversarial attacks and environmental variations. Through rigorous evaluation and adversarial testing, this research of ATM security systems, fostering a more secure financial landscape.

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Section: IImentioning

confidence: 99%

Unusual Human Activity Recognition in ATM’S

2024

IJRASET

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“…Supervised methods attempt to model normal and abnormal samples through data annotation during the training phase, which requires predefined abnormal behaviors according to different scenarios [1][2][3][4]. However, supervised ABD methods are difficult to generalize.…”

Section: Introductionmentioning

confidence: 99%

Video Surveillance for Indoor Office Environment Based on Object-Level Anomaly Detection

Chen

Zhang

Lin

2023

J. Phys.: Conf. Ser.

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Traditional methods of Abnormal Behavior Detection (ABD) process the surveillance video on frame-level, which ignores object-level abnormal behavior patterns. To address the problem, this paper presents Object-Level Anomaly Detection model (OLAD), which aims to model various normal behavior patterns of different objects and the normal interaction patterns between them. Specifically, OLAD introduces an encoding-embedding network to transform object-level information into the feature space. By integrating such information, OLAD processes both frame-level and object-level cues in the video for ABD. In addition, we construct our own dataset Northking2022 especially for office scenes because of the lack of public datasets for indoor office environments. Experimental results show that OLAD gains better performance on both the public benchmark and Northking2022.

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“…Interfacing small and multiple cameras enables 360°s urveillance and increases ATM security (Ray et al 2015). The algorithms like statistical property and standard deviation of moving objects can reduce the cost for a Camera with high resolution as it detects suspicious activity at low resolutions (Goswami et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A novel design and implementation of IoT based real-time ATM surveillance and security system

Gavaskar¹,

Ragupathy²,

Elango³

et al. 2021

Adv. in Comp. Int.

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Security and safety are a necessity for automated teller machines (ATM). The ATM security system is implemented using the Internet of things (IoT) and GPS (global positioning system). The main idea of this project is to develop an ATM surveillance and security system. In this project, when any physical attack against the ATM takes place, then information about the attack is sent using IoT and also alerts the surrounding area using a buzzer, at the same time the entire data from the sensors is sent to the developed mobile application and puts alert message to the bank officials. The officials who have control over the mobile application can control the Door through their mobile to lock from their location remotely. To prevent the escape of the thief chloroform connected to the controller through relay can also be sprayed inside the ATM by the officials remotely from their place using the mobile app. The Camera (ESP32) is used for live video coverage and to monitor the activity inside the ATM. The Camera will not only record the activity but also, transmit will live video taken inside the ATM and the ATM location as latitude and longitude are tracked using GPS. The system is connected to the Blynk mobile application. The sensor and GPS data are read by the microcontroller and these data are sent to the Blynk application. With the help of the Blynk application, the official who has access to it can control the relays and the respective devices connected to the relay to turn it ON or OFF. It can be used in many real-time applications such as banks, homes, and industries.

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Unusual event detection in low resolution video for enhancing ATM security

Cited by 8 publications

References 20 publications

Unusual Human Activity Recognition in ATM’S

Unusual Human Activity Recognition in ATM’S

Video Surveillance for Indoor Office Environment Based on Object-Level Anomaly Detection

A novel design and implementation of IoT based real-time ATM surveillance and security system

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