Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera
Unmanned aerial vehicles (UAVs) have been widely used in urban traffic supervision in recent years. However, the detection, tracking, and geolocation of moving vehicle based on the airborne platform suffer from small object sizes, complex scenes, and low-accuracy sensors. To address these problems,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8766100/ |
| _version_ | 1831636262801375232 |
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| author | Xiaoyue Zhao Fangling Pu Zhihang Wang Hongyu Chen Zhaozhuo Xu |
| author_facet | Xiaoyue Zhao Fangling Pu Zhihang Wang Hongyu Chen Zhaozhuo Xu |
| author_sort | Xiaoyue Zhao |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) have been widely used in urban traffic supervision in recent years. However, the detection, tracking, and geolocation of moving vehicle based on the airborne platform suffer from small object sizes, complex scenes, and low-accuracy sensors. To address these problems, this paper develops a framework for moving vehicle detecting, tracking, and geolocating based on a monocular camera, a GPS receiver, and inertial measurement units (IMUs) sensors. First, the method based on YOLOv3 was employed for vehicle detection due to its effectiveness and efficiency for small object detection in complex scenes. Then, a visual tracking method based on correlation filters is introduced, and a passive geolocation method is presented to calculate the GPS coordinates of the moving vehicle. Finally, a flight control method in terms of the previous image processing results is introduced to lead the UAV that is following the interesting moving vehicle. The proposed scheme has been built on a DJI M100 platform on which a monocular camera and a microcomputer Jetson TX1 are added. The experimental results show that this scheme is capable of detecting, tracking, and geolocating the interesting moving vehicle with high precision. The framework demonstrates its capacity in automatic supervision on target vehicles in real-world experiments, which suggests its potential applications in urban traffic, logistics, and security. |
| first_indexed | 2024-12-19T06:30:01Z |
| format | Article |
| id | doaj.art-6ff545654c4941469e34f26d856eb631 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T06:30:01Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6ff545654c4941469e34f26d856eb6312022-12-21T20:32:26ZengIEEEIEEE Access2169-35362019-01-01710116010117010.1109/ACCESS.2019.29297608766100Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular CameraXiaoyue Zhao0Fangling Pu1https://orcid.org/0000-0002-1490-0347Zhihang Wang2Hongyu Chen3Zhaozhuo Xu4School of Electronic Information, Wuhan University, Wuhan, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaElectrical Engineering Department, Stanford University, Stanford, CA, USAUnmanned aerial vehicles (UAVs) have been widely used in urban traffic supervision in recent years. However, the detection, tracking, and geolocation of moving vehicle based on the airborne platform suffer from small object sizes, complex scenes, and low-accuracy sensors. To address these problems, this paper develops a framework for moving vehicle detecting, tracking, and geolocating based on a monocular camera, a GPS receiver, and inertial measurement units (IMUs) sensors. First, the method based on YOLOv3 was employed for vehicle detection due to its effectiveness and efficiency for small object detection in complex scenes. Then, a visual tracking method based on correlation filters is introduced, and a passive geolocation method is presented to calculate the GPS coordinates of the moving vehicle. Finally, a flight control method in terms of the previous image processing results is introduced to lead the UAV that is following the interesting moving vehicle. The proposed scheme has been built on a DJI M100 platform on which a monocular camera and a microcomputer Jetson TX1 are added. The experimental results show that this scheme is capable of detecting, tracking, and geolocating the interesting moving vehicle with high precision. The framework demonstrates its capacity in automatic supervision on target vehicles in real-world experiments, which suggests its potential applications in urban traffic, logistics, and security.https://ieeexplore.ieee.org/document/8766100/Unmanned aerial vehicleYOLOv3object geolocationmoving vehicle tracking |
| spellingShingle | Xiaoyue Zhao Fangling Pu Zhihang Wang Hongyu Chen Zhaozhuo Xu Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera IEEE Access Unmanned aerial vehicle YOLOv3 object geolocation moving vehicle tracking |
| title | Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera |
| title_full | Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera |
| title_fullStr | Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera |
| title_full_unstemmed | Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera |
| title_short | Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera |
| title_sort | detection tracking and geolocation of moving vehicle from uav using monocular camera |
| topic | Unmanned aerial vehicle YOLOv3 object geolocation moving vehicle tracking |
| url | https://ieeexplore.ieee.org/document/8766100/ |
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