Roadway traffic monitoring from an unmanned aerial vehicle

2006 ◽  
Vol 153 (1) ◽  
pp. 11 ◽  
Author(s):  
B. Coifman ◽  
M. McCord ◽  
R. G. Mishalani ◽  
M. Iswalt ◽  
Y. Ji
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Traffic Monitoring ◽  
Aerial Vehicle

Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

Robotica ◽  
2020 ◽  
pp. 1-16
Author(s):  
Xiaofeng Liu ◽  
Jian Ma ◽  
Dashan Chen ◽  
Li-Ye Zhang
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Route Optimization ◽  
Road Segment ◽  
Solution Quality ◽  
Planning Approach ◽  
Aerial Vehicle ◽  
Road Segments

SUMMARY Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

Unmanned aerial vehicle scheduling problem for traffic monitoring

2018 ◽  
Vol 122 ◽  
pp. 15-23 ◽  
Author(s):  
Miao Li ◽  
Lu Zhen ◽  
Shuaian Wang ◽  
Wenya Lv ◽  
Xiaobo Qu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Traffic Monitoring ◽  
Vehicle Scheduling ◽  
Scheduling Problem ◽  
Aerial Vehicle

scholarly journals Object Tracking in Unmanned Aerial Vehicle Videos via Multifeature Discrimination and Instance-Aware Attention Network

2020 ◽  
Vol 12 (16) ◽  
pp. 2646
Author(s):  
Shiyu Zhang ◽  
Li Zhuo ◽  
Hui Zhang ◽  
Jiafeng Li
Keyword(s):  
Object Tracking ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Traffic Monitoring ◽  
Superior Performance ◽  
Visual Object ◽  
Attention Network ◽  
Bounding Box ◽  
Detection Stage ◽  
Aerial Vehicle

Visual object tracking in unmanned aerial vehicle (UAV) videos plays an important role in a variety of fields, such as traffic data collection, traffic monitoring, as well as film and television shooting. However, it is still challenging to track the target robustly in UAV vision task due to several factors such as appearance variation, background clutter, and severe occlusion. In this paper, we propose a novel two-stage UAV tracking framework, which includes a target detection stage based on multifeature discrimination and a bounding-box estimation stage based on the instance-aware attention network. In the target detection stage, we explore a feature representation scheme for a small target that integrates handcrafted features, low-level deep features, and high-level deep features. Then, the correlation filter is used to roughly predict target location. In the bounding-box estimation stage, an instance-aware intersection over union (IoU)-Net is integrated together with an instance-aware attention network to estimate the target size based on the bounding-box proposals generated in the target detection stage. Extensive experimental results on the UAV123 and UAVDT datasets show that our tracker, running at over 25 frames per second (FPS), has superior performance as compared with state-of-the-art UAV visual tracking approaches.

A Reliable Multi-Sensor Navigation System for an Unmanned Aerial Vehicle Realized through Integration of SINS with GPS, CNS and Doppler Radar

2013 ◽  
Vol 392 ◽  
pp. 312-318 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Navigation System ◽  
Doppler Radar ◽  
Low Cost ◽  
Inertial Navigation ◽  
Traffic Monitoring ◽  
Integrated Navigation ◽  
Low Weight ◽  
Aerial Vehicle ◽  
Local Filters

Contemporary importance of the unmanned aerial vehicle (UAV) both for military and civilian applications has prompted vigorous research related with guidance, navigation and control of these vehicles. The potential civilian uses for small low-cost UAVs are various like reconnaissance, surveillance, rescue and search, remote sensing, traffic monitoring, destruction appraisal of natural disasters, etc. One of the most crucial parts of UAVs missions is accurate navigation of the vehicle, i.e. the real time determination of its position, velocity and attitude. Generally highly accurate Strap down Inertial Navigation Systems (SINS) are too heavy to be flown on UAVs. Moreover highly accurate SINS are also highly expensive. Therefore the low-cost and low weight MEMS based SINS with a compromised precision are the viable option for navigation of UAVs. The errors in position, velocity, and attitude solutions provided by the MEMS based SINS grow unboundedly with the passage of time. To contain these growing errors, integrated navigation is the resolution. Complementary characteristics SINS and external non-inertial navigation aids like Global Positioning System (GPS), Celestial Navigation System (CNS) and Doppler radar make the integrated navigation system an appealing and cost effective solution. The non-inertial sensors providing navigation fixes must have low weight and volume to be suitable for UAV application. In this research work GPS, CNS and Doppler radar are used as external navigation aids for SINS. The navigation solutions of all contributing systems are fused using Federated Kalman Filter (FKF). Three local filters are employed for SINS/GPS, SINS/CNS and SINS/Doppler integration and subsequently information from all three local filters is fused to acquire a global solution. Moreover adaptive and fault tolerant filtering scheme has also been implemented in each local filter to isolate or accommodate any undesirable error or noise. Simulation for the presented architecture has validated the effectiveness of the scheme, by showing a substantial precision improvement in the solutions of position, velocity and attitude.

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle
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