A Pan-tilt Camera Control System of UAV Visual Tracking Based on Biomimetic Eye

Zou, Hairong; Zhang, Gong; Xie, Shaorong; Ding, Wei

doi:10.1109/robio.2006.340147

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…Therefore one has to lower the data-taking expectations finding a compromise between the two factors, which most of the time ends up reducing both the field of view and the resolution of the system. A smarter, though more complicated, strategy is to replace the static set-up with a dynamic one, effectively widening the field of view with a controlled rotation of the cameras aimed at following the targets, [24], [25], [26], [27], [28], [29], [30]. The dynamic set-up overcomes the limitation of the static one by actually breaking the link between the size of the field of view and the resolution of the system, which is now the only factor depending on the focal length.…”

Section: Introductionmentioning

confidence: 99%

CoMo: A Novel Comoving 3D Camera System

Cavagna

Xiao

Melillo

et al. 2021

IEEE Trans. Instrum. Meas.

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Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but at the same time we change in time the external parameters of the system, which have then to be calibrated frame-by-frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system home configuration (rotational stage at an angle equal to 0 • ) and combining this static information with the time dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%. The novelty of the work presented in this paper is not only on the system itself, but also on the approach we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies and that, for this reason, may be relevant for a broad audience.

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Section: Introductionmentioning

confidence: 99%

CoMo: A Novel Comoving 3D Camera System

Cavagna

Xiao

Melillo

et al. 2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…Therefore one has to lower the data-taking expectations finding a compromise between the two factors, which most of the time ends up reducing both the field of view and the resolution of the system. A smarter, though more complicated, strategy is to replace the static set-up with a dynamic one, effectively widening the field of view with a controlled rotation of the cameras aimed at following the targets, [24]- [30]. The dynamic setup overcomes the limitation of the static one by actually breaking the link between the size of the field of view and the resolution of the system, which is now the only factor depending on the focal length.…”

Section: Introductionmentioning

confidence: 99%

CoMo: A novel co-moving 3D camera system

Cavagna¹,

Xing²,

Melillo³

et al. 2021

Preprint

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Motivated by the theoretical interest in reconstructing long 3D trajectories of individual birds in large flocks, we developed CoMo, a co-moving camera system of two synchronized high speed cameras coupled with rotational stages, which allow us to dynamically follow the motion of a target flock. With the rotation of the cameras we overcome the limitations of standard static systems that restrict the duration of the collected data to the short interval of time in which targets are in the cameras common field of view, but at the same time we change in time the external parameters of the system, which have then to be calibrated frame-by-frame. We address the calibration of the external parameters measuring the position of the cameras and their three angles of yaw, pitch and roll in the system home configuration (rotational stage at an angle equal to 0 • ) and combining this static information with the time dependent rotation due to the stages. We evaluate the robustness and accuracy of the system by comparing reconstructed and measured 3D distances in what we call 3D tests, which show a relative error of the order of 1%. The novelty of the work presented in this paper is not only on the system itself, but also on the approach we use in the tests, which we show to be a very powerful tool in detecting and fixing calibration inaccuracies and that, for this reason, may be relevant for a broad audience.

show abstract

“…So it is a trend to integrate autonomous helicopters and visual sensors to accomplish tasks like reconnaissance, inspection and surveillance. Based on the mounting way of cameras, vision systems for autonomous helicopters can be divided into two categories: (1) Fixed camera [5][6][7] and (2) Pan-tilt camera [8,9]. In [5,6], visual servo methods are developed for autonomous helicopter to track features in the environment.…”

Section: Introductionmentioning

confidence: 99%

“…And in their system, the moving platform should be tracked while the helicopter is landing. In [8,9], a method inspired by biomimetic eye is developed to control the pan-tilt camera on the helicopter to track ground moving target continuously, smoothly, clearly and stably like human eyes. The vision system using a fixed camera is simple to implement, but the field-of-view of camera is coupled with the motion of helicopter.…”

Section: Introductionmentioning

confidence: 99%

A motion controller for a pan-tilt camera on an autonomous helicopter

Deng

Zhao

Hou

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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In this paper, a motion controller for a pan-tilt camera mounted on an autonomous helicopter is presented. The motion planner is designed according to the kinematics of the pan-tilt camera. However, the solution of the inverse kinematics of the pan-tilt camera is not unique. To deal with this situation, a decision maker is designed. The decision maker makes use of a cost function to decide which solution should be chose. And the error signal is defined as the difference between pantilt joint angles and the desired joint angles. The dynamics of the error system is derived, and proportional controllers are designed according to the error dynamics to control the pan and tilt joints respectively. This system is validated in a virtual reality environment, and the simulation results show that this system can track the target successfully.

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A Pan-tilt Camera Control System of UAV Visual Tracking Based on Biomimetic Eye

Cited by 10 publications

References 20 publications

CoMo: A Novel Comoving 3D Camera System

CoMo: A Novel Comoving 3D Camera System

CoMo: A novel co-moving 3D camera system

A motion controller for a pan-tilt camera on an autonomous helicopter

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