Situation Control of Unmanned Aerial Vehicles for Road Traffic Monitoring

Kim, Nikolai Vladimirovich; Chervonenkis, Mikhail

doi:10.5539/mas.v9n5p1

Cited by 24 publications

(17 citation statements)

References 16 publications

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“…Deep learning methods applied on images captured from UAVs have an actual significance in CPSs. For instance, previous studies proposed the deep learning approach for detecting and counting cars for images captured from UAVs [161], road traffic monitoring from UAVs [162], and inspecting civil infrastructure systems [28,163].…”

Section: ) Effective Visual Data Analysis Algorithmsmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

197

100

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Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. M 2 respectively. However, as shown in Fig.1, both technologies provide two extremes for surveillance with respect to accuracy versus intrusive deployment and maintenance. SRS provides slow large-scale monitoring, while CSNs require widespread deployment and constant human intervention for setup and maintenance, which may not be feasible particularly in harsh environments. On the one hand, SRS cannot be used for finegrain monitoring, such as monitoring of structural objects, with high accuracy, which limits its usage in many applications that require close monitoring and context detection [5]. On the other hand, traditional sensor networks (SNs) are spreading in countless number of Internet of Things (IoT) applications to provide automatic monitoring of objects, humans, and/or infrastructure. SNs, however, require permanent deployment of large magnitude of localized sensors, intrusively to the environment in many cases, and also require constant human intervention to manage and maintain. The proliferation of lowcost UAVs [6] is emerging to provide innovative ways of low altitude sensing with zero-deployment (i.e., no fixed deployment requirements) that can fundamentally change the way such applications are designed.Similar to surveillance applications, UAV technologies have progressed significantly in several applications in the past decade, making commercial and civil applications feasible and compelling. As it occurs with many disruptive technolo...

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Section: ) Effective Visual Data Analysis Algorithmsmentioning

confidence: 99%

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Shakeri

Al-Garadi

Badawy

et al. 2019

IEEE Commun. Surv. Tutorials

197

100

View full text Add to dashboard Cite

show abstract

“…Deep learning techniques applied on images taken from UAVs have also gained a lot of importance in monitoring and search and rescue applications, such as jellyfish monitoring [70], road traffic monitoring from UAVs [71], assisting avalanche search and rescue operations with UAV imagery [72], and terrorist identification [73]. In [72,73], the use of pretrained CNN models for feature extraction is worth noting again.…”

Section: With Image Sensorsmentioning

confidence: 99%

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

et al. 2017

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Deep learning is recently showing outstanding results for solving a wide variety of robotic tasks in the areas of perception, planning, localization, and control. Its excellent capabilities for learning representations from the complex data acquired in real environments make it extremely suitable for many kinds of autonomous robotic applications. In parallel, Unmanned Aerial Vehicles (UAVs) are currently being extensively applied for several types of civilian tasks in applications going from security, surveillance, and disaster rescue to parcel delivery or warehouse management. In this paper, a thorough review has been performed on recent reported uses and applications of deep learning for UAVs, including the most relevant developments as well as their performances and limitations. In addition, a detailed explanation of the main deep learning techniques is provided. We conclude with a description of the main challenges for the application of deep learning for UAV-based solutions.

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“…In this study a cascading approach to the vehicle recognition has been defined. The works (Kim and Chervonenkis, 2015;Abramov et al, 2015) have suggested using the image segmentation into superpixels followed by their association in regions, this approach is also used in proposed algorithm herein. These works were inspired by (Choi and Yang, 2009) paper which applies mean shift segmentation in the Luv color space in order to extract blobs (superpixels) of the image.…”

Section: Introductionmentioning

confidence: 99%

“…The mentioned work by (Kim and Chervonenkis, 2015) describes in detail the methodology of the use of UAV for road traffic monitoring and proposes an approach based on the recognition and classification of severity traffic situation. In this study we described the UAV automatic control.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection in Aerial Traffic Monitoring

Sincha¹,

Chervonenkis²,

Skribtsov³

2016

American Journal of Applied Sciences

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This work describes a cascade detection of vehicles in Unmanned Aerial Vehicle (UAV) images and videos. There are some new approaches used in the detection. In particular, the Region of Interest (ROI) search is not only based on GIS and navigation data, but also employs visual method based on rapid image segmentation and road detection. The work also suggests doing ROI segmentation by the superpixel technique and trainable four-level cascade detector that uses artificial neural networks as classifiers. Characteristics of the being analyzed regions (combined superpixels) are based on geometric and texture features, as well as on deep features extracted from the image patches by nonlinear auto encoders. To improve the detection quality of the moving vehicles a separate stage of the detector based on optical flow analysis was introduced. Proposed detection algorithm was benchmarked on the real UAV videos and showed the sufficiently high accuracy. Performance of the algorithm allows supposing the on-board usage.

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Situation Control of Unmanned Aerial Vehicles for Road Traffic Monitoring

Cited by 24 publications

References 16 publications

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles

Vehicle Detection in Aerial Traffic Monitoring

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