A Surveillance Video Real-Time Object Detection System Based on Edge-Cloud Cooperation in Airport Apron

Lyu, Zonglei; Luo, Jia

doi:10.3390/app121910128

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…For recognizing the objects in real time, an edge-based detection strategy is introduced in the YOLO algorithm. Thus, the motion of the abandoned object is accurately recognized from the video [13].…”

Section: Introductionmentioning

confidence: 99%

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Jayaram C V

2024

IJRITCC

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Co-located pattern recognition is the process of identifying the sequence of patterns occurring in surveillance videos. In greater part of the existing works, the detection of rare and popular events for effective co-located pattern recognition is not concentrated. Therefore, this paper presents the automatic discovery of the co-located patterns based on rare and popular events in the video. First, the video is converted to frames, and the keyframes are preprocessed. Then, the foreground and background of the frames are estimated, and the rare and popular events are grouped using Maximum-Minimum Pixel-Per-Inch Density-Based Spatial Clustering of Applications with Noise (Max-MinPPI-DBSCAN). From the grouped image, the object detection and mapping are done, and the patch is extracted from it. Next, the edges are detected and from that, for the moving objects, motion is estimated by the Kullback-Leibler Kalman Filter (KLKF). Also, for non-moving objects, the objects/persons are tracked. From the motion estimated and tracked data, time series features are extracted. Then, the optimal features are selected using the Dung Beetle State Transition Probability Optimizer (DBSTPO). Finally, the co-located pattern is classified using a Generalized Recurrent Extreme Value Neural Network (GREVNN), and the alert message is given to the authorities. Hence, the proposed model selected the features in 53239.44ms and classified the event with 99.0723% accuracy and showed better performance than existing works.

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Section: Introductionmentioning

confidence: 99%

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Jayaram C V

2024

IJRITCC

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“…An airport terminal monitoring system comprises a digital distributed network infrastructure, encompassing management servers, storage servers, digital cameras, and other essential equipment [1]. At present, the monitoring and management systems used in the airports can realize the monitoring of video loss, disconnection of encoder equipment, server failure, hard disk failure, and other equipment status, but cannot realize the monitoring of some soft faults such as video image quality problems [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Reactive Deep Learning-Based Model for Quality Assessment in Airport Video Surveillance Systems

Liu,

Pan,

Fan

2024

Electronics

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Monitoring the correct operation of airport video surveillance systems is of great importance in terms of the image quality provided by the cameras. Performing this task using human resources is time-consuming and usually associated with a delay in diagnosis. For this reason, in this article, an automatic system for image quality assessment (IQA) in airport surveillance systems using deep learning techniques is presented. The proposed method monitors the video surveillance system based on the two goals of “quality assessment” and “anomaly detection in images.” This model uses a 3D convolutional neural network (CNN) for detecting anomalies such as jitter, occlusion, and malfunction in frame sequences. Also, the feature maps of this 3D CNN are concatenated with feature maps of a separate 2D CNN for image quality assessment. This combination can be useful in improving the concurrence of correlation coefficients for IQA. The performance of the proposed model was evaluated both in terms of quality assessment and anomaly detection. The results show that the proposed 3D CNN model could correctly detect anomalies in surveillance videos with an average accuracy of 96.48% which is at least 3.39% higher than the compared methods. Also, the proposed hybrid CNN model could assess image quality with an average correlation of 0.9014, which proves the efficiency of the proposed method.

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“…The most prevalent IoST devices, including IP cameras and digital or network video recorders (DVR/NDR), are widely deployed not only in public places to monitor activities and behaviors that may pose threats to assets and people, such as thefts and criminal actions, but also as part of critical infrastructure deployments to ensure the security and safety of operational processes. IP cameras stream video and images to the cloud or internal servers and can be found in strategic locations such as manufacturing facilities, hospitals, police departments, financial markets, and airports [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Exploitation Techniques of IoST Vulnerabilities in Air-Gapped Networks and Security Measures—A Systematic Review

Hamada,

Kuzminykh

2023

Signals

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IP cameras and digital video recorders, as part of the Internet of Surveillance Things (IoST) technology, can sometimes allow unauthenticated access to the video feed or management dashboard. These vulnerabilities may result from weak APIs, misconfigurations, or hidden firmware backdoors. What is particularly concerning is that these vulnerabilities can stay unnoticed for extended periods, spanning weeks, months, or even years, until a malicious attacker decides to exploit them. The response actions in case of identifying the vulnerability, such as updating software and firmware for millions of IoST devices, might be challenging and time-consuming. Implementing an air-gapped video surveillance network, which is isolated from the internet and external access, can reduce the cybersecurity threats associated with internet-connected IoST devices. However, such networks can also be susceptible to other threats and attacks, which need to be explored and analyzed. In this work, we perform a systematic literature review on the current state of research and use cases related to compromising and protecting cameras in logical and physical air-gapped networks. We provide a network diagram for each mode of exploitation, discuss the vulnerabilities that could result in a successful attack, demonstrate the potential impacts on organizations in the event of IoST compromise, and outline the security measures and mechanisms that can be deployed to mitigate these security risks.

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A Surveillance Video Real-Time Object Detection System Based on Edge-Cloud Cooperation in Airport Apron

Cited by 9 publications

References 23 publications

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

A Reactive Deep Learning-Based Model for Quality Assessment in Airport Video Surveillance Systems

Exploitation Techniques of IoST Vulnerabilities in Air-Gapped Networks and Security Measures—A Systematic Review

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