A Target Localization Method for UAV Image Sequences Based on DEM Matching

Zhuo, Jiajing; Sun, Lanqiong; Yang, Yi; Zhao, Xingyu

doi:10.1109/iscid.2016.2058

Cited by 4 publications

(1 citation statement)

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“…Others sought faster methods by introducing the digital terrain model (DTM). The aerial images were corrected, and the object location was calculated by matching the DTM data [26], [27]. The accuracy of the results was significantly affected by the reference data, and the ground reference points were shown to be indispensable.…”

Section: Object Geolocationmentioning

confidence: 99%

Detection, Tracking, and Geolocation of Moving Vehicle From UAV Using Monocular Camera

Zhao

Wang

et al. 2019

IEEE Access

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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. INDEX TERMS Unmanned aerial vehicle, YOLOv3, object geolocation, moving vehicle tracking.

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Section: Object Geolocationmentioning

confidence: 99%