Remote Estimation of Target Height from Unmanned Aerial Vehicle (UAV) Images

This paper focuses on how the height of a target can be swiftly estimated using images acquired by a digital camera installed into moving platforms, such as unmanned aerial vehicles (UAVs). A pinhole camera model after distortion compensation was considered for this purpose since it does not need extensive processing nor vanishing lines. The pinhole model has been extensively employed for similar purposes in past studies but mainly focusing on fixed camera installations. This study analyzes how to tailor the pinhole model for gimballed cameras mounted into UAVs, considering camera parameters and flight parameters. Moreover, it indicates a solution that foresees correcting only a few needed pixels to limit the processing overload. Finally, an extensive analysis was conducted to define the uncertainty associated with the height estimation. The results of this analysis highlighted interesting relationships between UAV-to-target relative distance, camera pose, and height uncertainty that allow practical exploitations of the proposed approach. The model was tested with real data in both controlled and uncontrolled environments, the results confirmed the suitability of the proposed method and outcomes of the uncertainty analysis. Finally, this research can open consumer UAVs to innovative applications for urban surveillance.

Metode Pinhole Model untuk Menentukan Jarak Perpindahan Kendaraan dalam Video

Intelligent Transportation System is a solution to overcome the problem of transportation. The purpose of this study was to design a system to calculate the distance of vehicle movement in the video. One example of application of intelligent transport system is to calculate the speed of a vehicle that can be used in traffic engineering. Parameters used to obtain the speed of the vehicle is the distance parameter. The distance of vehicle movement in the video can be obtained by using a pinhole camera calibration model. An appropriate calibration of camera can provide the intended parameter. There are several stages to perform calculation of the distance of object movement. The first stage was a detection of vehicle within the frame. The detection was required to obtain the center point of vehicle object. The next stage was to detect an object within the frame. This was performed to understand the movement of object from one frame to another frame. The data used for the study were video data with mov format. The result of research showed that the method of pinhole model was applicable to calculate the distance of object movement in video. The application of this method can help in calculating the object distance without manual calculation.

Компенсация погрешностей модели центральной проекции в трехмерных сканерах

Pinhole model measurement errors in 3D triangulation based scanners is studied in the present work. Pupil aberrations of the scanner lenses are shown to cause noticeable errors in determining the coordinates of surface points. The requirements for aberration characteristics are formulated to minimize the error of 3D scanners. A modification of the pinhole model allowing a decrease in the error induced by pupil aberration of lenses of triangulation-based scanners is proposed as well.