A Complex for Monitoring Transport Infrastructure Facilities Based on Video Surveillance Cameras and Laser Scanners

Gura, Dmitry; Markovskii, I. G.; Khusht, N. I.; Pshidatok, Saida

doi:10.1016/j.trpro.2021.02.130

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…The construction of IoT depends on physical signal sensor, radio frequency identification (RFID) [ 8 ], and global positioning system (GPS) [ 9 ] to acquire target information in real time. After that, it realizes the connection between the target and the network and then achieves the interconnection among everything and intelligent recognition and management of the interconnection between human and things, which is the extension and expansion of Internet technology [ 10 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

Hong-bin

Bai

2022

Computational Intelligence and Neuroscience

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This work is conducted to deal with the information security of enterprise management under the background of current global informatization, popularize the modern Internet of Things (IoT) management technology of enterprises, and maintain the information security of enterprises and provide modern upgrading means for enterprise management. In this work, it firstly introduces the application scenarios of current Internet and Artificial Intelligence (AI) technology and expounds the IoT technology. Secondly, the enterprise management platform is designed, the requirements of enterprise modern management are analyzed, and then the design requirements of system functions and the design of the information security architecture of the IoT are proposed. Furthermore, an enterprise information security management platform is designed, which covers four parts: IoT data mining management, equipment management, key management, and database management. In addition, the performance of the security management platform is tested from four parts: concurrency testing, stress testing, large data volume testing, and security testing. The research results show that the IoT-based enterprise information security management platform designed in this work under the background of AI has perfect functions and stable performance of each module. Concurrency testing, stress testing, large data volume testing, and stability testing are performed on it, and the success rate of the platform in each task reaches 100%. The average response time of concurrent testing and stress testing is about 0.13 seconds, and that of the event entry events is 0.25 seconds. The central processing unit (CPU) occupancy rate in each monitoring task is always lower than 20%. Therefore, it is determined that the performance of the IoT-based enterprise information security management platform designed in this work is sufficient to meet the daily management of enterprises. This work can provide a guarantee for enterprise information security management using AI technology, setting an example for future related research.

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Section: Introductionmentioning

confidence: 99%

Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

Hong-bin

Bai

2022

Computational Intelligence and Neuroscience

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“…The use of real-time video analysis is not a new task from the research point of view. There are a number of context-oriented solutions, e.g., fire and smoke detection (Saponara et al, 2020), monitoring engineering structures as a part of monitoring transport infrastructure facilities (Gura et al, 2021), automatic road environment classification (Tang & Breckon, 2011). The area dealing with traffic sign recognition has been developing rapidly, especially in the last 15 years.…”

Section: Introductionmentioning

confidence: 99%

Use of Intelligent Navigation and Crowd Collaboration for Automated Collection of Data on Transport Infrastructure

Tvrzský¹

2022

AIP

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The article briefly presents the main results of an applied research project to the professional public. The project output is a solution that enables the recognition of selected types of traffic signs using artificial intelligence for image recognition. This computationally intensive process is implemented in mobile phones. In order to achieve the involvement of the general public in the collection of data on transport infrastructure, the entire solution is part of navigation for mobile phones and supported by two functions that motivate users to collect data, i.e., scan the area in front of the vehicle with the phone's camera. The first function is the projection of the route into the real environment (the so-called augmented reality mode), and the second function is the possibility of video recording the drive. The video recording is cryptographically signed to ensure authenticity in administrative or judicial proceedings, e.g., when proving the course and circumstances of a traffic accident. The collection of data on transport infrastructure is completely anonymous in compliance with applicable laws. The data about recognized traffic signs will not only serve the navigation provider to improve the user experience but the processed data will also be exported to community-created world maps (project OpenStreetMap).

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“…is system usually calls the helpline in case of any emergency or accident encountered by travellers. It uses surveillance cameras mounted on roads to check violations [9,10]. It incorporates di erent applications from basic to advanced, i.e., navigation systems for vehicles, variable message signs, and surveillance cameras on the road are some of its applications [11][12][13][14].…”

mentioning

confidence: 99%

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

Waris

Asif

Ahmad

et al. 2022

Mathematical Problems in Engineering

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An intelligent transportation system (ITS) is an advanced application that supports multiple transport and traffic management modes. ITS services include calling for emergency rescue and monitoring traffic laws with the help of roadside units. It is observed that many people lose their lives in motorbike accidents mainly due to not wearing helmets. Automatic helmet violation detection of motorcyclists from real-time videos is a demanding application in ITS. It enables one to spot and penalize bikers without a helmet. So, there is a need to develop a system that automatically detects and captures motorbikers without a helmet in real time. This work proposes a system to detect helmet violations automatically from surveillance videos captured by roadside-mounted cameras. The proposed technique is based on faster region-based convolutional neural network (R-CNN) deep learning model that takes video as an input and performs helmet violation detection to take necessary actions against traffic rule violators. Experimental analysis shows that the proposed system gives an accuracy of 97.69% and supersedes its competitors.

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A Complex for Monitoring Transport Infrastructure Facilities Based on Video Surveillance Cameras and Laser Scanners

Cited by 11 publications

References 9 publications

Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology

Use of Intelligent Navigation and Crowd Collaboration for Automated Collection of Data on Transport Infrastructure

CNN-Based Automatic Helmet Violation Detection of Motorcyclists for an Intelligent Transportation System

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