A survey of unmanned aerial vehicles (UAVs) for traffic monitoring

Kanistras, Konstantinos; Martins, Gonçalo; Rutherford, Matthew J.; Valavanis, Kimon P.

doi:10.1109/icuas.2013.6564694

Cited by 210 publications

(57 citation statements)

References 15 publications

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“…As ground-based detection devices can be prone to failure and are too costly to install and maintain in some countries, there is a need for alternative monitoring technologies, such as through GPS data from cell phones [Herrera et al, 2010] or with aerial or high-resolution satellite images, and even lower-resolution satellite images [Larsen et al, 2009, Eikvil et al, 2009. There is also potential for using drones [Kanistras et al, 2015]. With remote sensing, a large number of roads can be covered at the same instance, many of which are not equipped with costly sensors [Larsen et al, 2009, Eikvil et al, 2009] (e.g., rural or remote roads).…”

Section: Background: Traffic Monitoring and Freight Surveyingmentioning

confidence: 99%

Truck traffic monitoring with satellite images

Kaack

Chen

Morgan

2019

Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies

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The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low-and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries. arXiv:1907.07660v1 [cs.CY]

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Section: Background: Traffic Monitoring and Freight Surveyingmentioning

confidence: 99%

Truck traffic monitoring with satellite images

Kaack

Chen

Morgan

2019

Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies

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“…Each invocation of the application (period of one second) cycles through the following steps: (i) retrieve a raw image from a camera, (ii) compresses it to a JPEG format, (iii) upload the image file to the base station through FTP and finally (iv) write a log via HTTP post. This type of application model (image capture → processing → communication) can be found in modern unmanned aerial vehicles (UAVs) that are used for surveillance or environmental studies [19]. Figure 7: The normal execution flow of the target application.…”

Section: System Implementationmentioning

confidence: 99%

Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System

Yoon

Mohan

Choi

et al. 2017

Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

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Existing techniques used for intrusion detection do not fully utilize the intrinsic properties of embedded systems. In this paper, we propose a lightweight method for detecting anomalous executions using a distribution of system call frequencies. We use a cluster analysis to learn the legitimate execution contexts of embedded applications and then monitor them at run-time to capture abnormal executions. We also present an architectural framework with minor processor modifications to aid in this process. Our prototype shows that the proposed method can effectively detect anomalous executions without relying on sophisticated analyses or affecting the critical execution paths. IntroductionAn increasing number of attacks are targeting embedded systems [21,37] that compromise the security, and hence safety, of such systems. It is not an easy task to retrofit embedded systems with security mechanisms that were developed for more general purpose scenarios since the former (a) have constraints in processing power, memory, battery life, etc. and (b) are required to meet stringent requirements such as timing constraints.Traditional behavior-based intrusion detection systems (IDS) [10] rely on specific signals such as network traffic [15,39], control flow [1,8], system calls [14,32,5], etc. The use of system calls, especially in the form of sequences [14,16,44,29,12,40], has been extensively studied in behavior-based IDSes for general purpose systems since many malicious activities often use system calls to execute privileged operations on system resources. Because server, desktop and mobile applications exhibit rich, wildly varying behaviors across executions, such IDSes need to rely either (a) on complex models of normal behavior, which are expensive to run and thus unsuitable for an embedded system, or (b) on simple, partial models, which validate only small windows of the application execution at a time. This opens the door for attacks where variations of a valid execution sequence are replayed with slightly different parameters to achieve a malicious goal; on the other hand, the application would not execute that sequence of operations in a normal manner, every time.

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“…Interestingly, it was found that most survey works are focusing on vision-based vehicle classification techniques [7][8] [9][10] [11] [12] overlooking numerous other emerging vehicle classification solutions. Although there are some works that provide a review of vehicle classification systems based on different types of sensors, these papers discuss only a particular type of vehicle classification system such as UAVs [13] [14]. A comprehensive review on traffic monitoring systems have been performed recently [15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

147

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A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality-vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of the vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.

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A survey of unmanned aerial vehicles (UAVs) for traffic monitoring

Cited by 210 publications

References 15 publications

Truck traffic monitoring with satellite images

Truck traffic monitoring with satellite images

Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

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