Application of Unmanned Aerial Vehicles in Smart Cities using Computer Vision Techniques

Shirazi, Mohammad Shokrolah; Patooghy, Ahmad; Shisheie, Reza; Haque, Munirul

doi:10.1109/isc251055.2020.9239054

Cited by 11 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the data collection and analysis were performed using software (sky software and PTV Vissim 2020) instead of actual drones. Shirazi et al [23] developed a deep visual tracking system for intersection monitoring (vehicle detection and tracking) using drones and CV techniques. The tracking system is also improved using the optical flow method.…”

Section: A Transportationmentioning

confidence: 99%

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

Drones, or Unmanned Aerial Vehicles (UAVs), are among the most beneficial and emerging technologies, with a wide range of applications that can support the sustainability concerns of smart cities and ultimately improve citizens' quality of life. The goals of this systematic review were to explore the involvement of surveillance drones in smart cities in terms of application status, application areas, proposed models, and characteristics of drones. We conducted this systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We systematically searched the Web of Science and Scopus for journal articles and conference papers written in English and published up to August 2021. Of the 323 records identified, 43 met the inclusion criteria. Findings showed that surveillance drones were used in seven distinct research fields (transportation, environment, infrastructure, object or people detection, disaster management, data collection, and other applications). Air pollution and traffic monitoring were the dominant application areas. The majority of reviewed models were based on the application of rotary-wing single-drones with the camera as the aerial sensor. Reviewed models showed that the adoption of a single or multiple UAVs, either as a stand-alone technology or integrated with other technologies (e.g., internet of things, wireless sensor networks, convolutional neural networks, artificial intelligence, machine learning, computer vision, cloud computing, web applications), can offer efficient and sustainable solutions compared to conventional surveillance methods. This review can benefit academic researchers and practitioners.

show abstract

Section: A Transportationmentioning

confidence: 99%

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Driving behavior at road intersections is a well-studied topic. It has been used for driver profiling and macro analysis, including trajectory prediction, turning recognition, and turning forecast [5], [6], [8]- [15].…”

Section: Related Workmentioning

confidence: 99%

“…In [6], the authors proposed an Unmanned Aerial Vehicle-based video system for monitoring intersections. The analysis was of two levels, the online level served to estimate vehicle speeds and headways, and the offline was used for trajectory analysis.…”

Section: Related Workmentioning

confidence: 99%

Video Analysis and Rule-Based Reasoning for Driving Maneuver Classification at Intersections

et al. 2022

View full text Add to dashboard Cite

We propose a system for monitoring the driving maneuver at road intersections using rule-based reasoning and deep learning-based computer vision techniques. Along with detecting and classifying turning movements online, the system also detects violations such as ignoring STOP signs and failing to yield the right-of-way to other drivers. There is no distinction between temporarily and permanently stopped vehicles in the majority of frameworks proposed in the literature. Therefore, to conduct an accurate right-of-way study, permanently stopped vehicles should be excluded not to confound the results. Moreover, we also propose in this work a low-cost Convolutional Neural Network (CNN)-based object detection framework able to detect moving and temporally stopped vehicles. The detection framework combines the reasoning system with background subtraction and a CNN-based object detector. The obtained results are promising. Compared to the conventional CNN-based methods, the detection framework reduces the execution time of the object detection module by about 30% (i.e., 54.1 instead of 75ms/image) while preserving the same detection reliability. The accuracy of trajectory recognition is 95.32%, that of the zero-speed detection is 96.67%, and the right-of-way detection was perfect.

show abstract

“…[8]. Moreover, in applications such as smart cities, UAVs need to interact with multiple ground-based entities for tasks such as energy-harvesting, reliable ground-entity localization, in addition to communication [9,10]. The spatial co-ordinate system used to locate a UAV needs to be compatible with the co-ordinate system that locates the on-ground entity.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta

Fernando

2022

Drones

View full text Add to dashboard Cite

This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.

show abstract

Application of Unmanned Aerial Vehicles in Smart Cities using Computer Vision Techniques

Cited by 11 publications

References 19 publications

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

Involvement of Surveillance Drones in Smart Cities: A Systematic Review

Video Analysis and Rule-Based Reasoning for Driving Maneuver Classification at Intersections

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Contact Info

Product

Resources

About