Smart traffic monitoring system using Unmanned Aerial Vehicles (UAVs)

Khan, Navid Ali; Jhanjhi, N. Z.; Brohi, Sarfraz Nawaz; Usmani, Raja Sher Afgun; Nayyar, Anand

doi:10.1016/j.comcom.2020.04.049

Cited by 160 publications

(51 citation statements)

References 33 publications

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“…It can monitor the transportation network, channel traffic and monitor events in real-time [128]. [129] introduced a UAV intelligent traffic monitoring system based on five generation (5G) technology, which overcomes the static characteristics and other limitations of traditional systems and greatly reduces the incidence of traffic accidents. [130] proposed a UAV-based vehicle detection and tracking system, which can generate vehicle dynamic information in real-time.…”

Section: E) Research Trends and Future Insightsmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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The dynamic uncertain environment and complex tasks determine that the unmanned aerial vehicle (UAV) system is bound to develop towards clustering, autonomy，and intelligence. In this paper, we present a comprehensive survey of UAV swarm intelligence from the hierarchical framework perspective. Firstly, we review the basics and advances of UAV swarm intelligent technology. Then we look inside to investigate the research work by classifying UAV swarm intelligence research into five layers, i.e., decision-making layer, path planning layer, control layer, communication layer, and application layer. Furthermore, the relationship between each level is explicitly illustrated, and the research trends of each layer are given. Finally, limitations and possible technology trends of swarm intelligence are also covered to enable further research interests. Through this in-depth literature review, we intend to provide novel insights into the latest technologies in UAV swarm intelligence.

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Section: E) Research Trends and Future Insightsmentioning

confidence: 99%

UAV Swarm Intelligence: Recent Advances and Future Trends

2020

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“…Consequently, the proposed method took advantage of accurate object detection and dynamic positioning from YOLO while using the KCF for speed and locking to a single target. Although there are many public data sets of human [4], car [3] for target tracking, there is no data set consisting only of UAVs concerning our specific problem. For that reason, the ATAUAV dataset which is one of our contributions is developed as a benchmark dataset for future scientific studies.…”

Section: Yolo Central Coordinatesmentioning

confidence: 99%

“…Most recently developed UAVs are equipped with a static or nonstationary camera; the operator can watch the camera footage in real-time through video streaming while the UAV is in flight [1], [2]. Advancing embedded high-speed cameras, these robotic systems enable the automatic detection and tracking of moving objects via ground control stations (GCSs) [3], such as cars [3], humans [4], and aircraft [5], in different applications [6]. However, tracking an object in video frames captured by fast-moving cameras or other targets remain a challenging problem in computer vision [1].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Moving UAV Tracking by Another UAV on Low-Cost Hardware and a New Ground Control Station

2020

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Automatic flying target detection and tracking in video sequences acquired from a camera mounted on another Unmanned Aerial Vehicle (UAV) is a challenging task due to the presence of nonstationary cameras in the system, dynamic motion of the moving target, and high-cost computation for realtime applications. In this paper, our aim is to automatically detect and track moving UAV by another one while simultaneously flying in the air. In order to provide efficiently in real-time applications, we develop a vision-based low-cost hardware system integrated with an independent ground control station. We initially created a new public dataset called ATAUAV that includes different types of UAV images obtained from videos recording in our experiments and searches on Google Images for the training process. Deep learningbased YOLOv3-Tiny (You Only Look Once) is used for target detection with the highest accuracy and fastest results. Kernelized Correlation Filter (KCF) adapted with YOLO, which runs on low-cost hardware, is used for real-time detected target tracking. We compared the performance of the proposed approach with different tracking algorithms. Experimental results show that the proposed approach provides the highest accuracy rate as 82.7% and a mean fps speed as 29.6 on CPU. The dataset can be downloaded at http://cogvi.atauni.edu.tr/ResearchLab/PageDetail/Our-ATAUAVs-Dataset-86. INDEX TERMS Artificial neural networks, computer vision, kcf, object detection, object recognition, target tracking, unmanned aerial vehicles, yolo This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…Recent advancements in Unmanned Ariel Vehicles (UAVs) or Drones, along with the development of communication technology, have changed and led to the emergence of various civilian applications such as emergency medical equipment deliveries [ 1 ], real-time monitoring in mining environmental [ 2 ], precision agriculture and crop monitoring [ 3 ] and traffic monitoring in smart cities [ 4 ]. Moreover, one of the most important applications of drones is in cellular networks, where the drones can act as a flying base station to enhance coverage and improve the quality of communication services for the ground users.…”

Section: Introductionmentioning

confidence: 99%

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Fotouhi

Ding

Hassan

2021

Sensors

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In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

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Smart traffic monitoring system using Unmanned Aerial Vehicles (UAVs)

Cited by 160 publications

References 33 publications

UAV Swarm Intelligence: Recent Advances and Future Trends

UAV Swarm Intelligence: Recent Advances and Future Trends

Vision-Based Moving UAV Tracking by Another UAV on Low-Cost Hardware and a New Ground Control Station

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

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