Relative Position Of Camera Research Articles

Laser-tracker-based reference measurement for geometric calibration of phase-measuring deflectometry with active display registration

Abstract. Phase-measuring deflectometry (PMD) with active display registration (ADR) is a ray-optics-based technique for the shape measurement of specular surfaces. To obtain quantitative results, the relative position of the cameras of the PMD–ADR setup needs to be determined by geometric calibration. Geometric calibration can be performed by inserting a planar mirror into the setup that brings all camera fields of view to overlap on an active pattern display. The mirror is tilted to multiple positions and each time the cameras capture the displayed images, which yields sufficient data to obtain the relative camera positions and the positions of the mirror. In this article, we give a more detailed description of PMD–ADR and its calibration. We also implement a laser-tracker-based reference method to measure the mirror positions and use its result to expose systematic errors in the geometric calibration.

Evaluation of Camera Pose Estimation Using Human Head Pose Estimation

We introduce and evaluate a novel camera pose estimation framework that uses the human head as a calibration object. The proposed method facilitates extrinsic calibration from 2D input images (NIR and/or RGB), while merely relying on the detected human head, without the need for depth information. The approach is applicable to single cameras or multi-camera networks. Our implementation uses a fine-tuned deep learning-based 2D human facial landmark detector to estimate the 3D human head pose by fitting a 3D head model to the detected 2D facial landmarks. Our work focuses on an evaluation of the proposed approach on real multi-camera recordings and synthetic renderings to determine the accuracy of the pose estimation results and their applicability. We assess the robustness of our method against different input parameters, such as varying relative camera positions, variations of head models, face occlusions (by masks, sun glasses, etc.), potential biases and variance among humans. Based on the experimental results, we expect our approach to be effective for numerous use cases including automotive attention monitoring, robotics, VR/AR and other scenarios where ease of handling outweighs accuracy.

Influence of camera model and alignment on the performance of paired camera stations

AbstractThe probability of obtaining images of target species may vary across camera models or relative position of cameras at survey locations. Alignment of cameras within paired camera stations (hereafter, stations) could affect species detection due to issues with image exposure. We quantified effects of 3 camera models and alignment (staggered, offset by a perpendicular distance of 4.6 m, and aligned, directly facing one another) on camera performance in a station design. Mean exposure events (flash from one camera overexposes or underexposes pictures) at aligned stations was 3.93 (SE = 1.01; n = 40), whereas no exposure events were documented at staggered (n = 36) stations. Overall frequency of exposure events of mammal images at aligned cameras was 44% (68 exposure events/153 images). On average, 8% (range 0−35%) of mammal images from aligned stations were exposure events. We detected no difference (P = 0.88) in exposure events among paired camera models. Further, we detected no overall differences (P ≥ 0.07) in paired camera performance (i.e., number of mammal images over survey interval) between aligned or staggered stations, though reliability (i.e., percentage of camera stations that lasted entire survey interval) varied (P ≤ 0.001) between model types. Research deploying 2 cameras within a camera station framework can eliminate exposure events by using a staggered camera alignment without affecting the number of usable mammal photos. Rigorous field testing prior to deployment of stations is warranted to optimize reliability. One of our low‐cost models performed as well as a more expensive model within our paired camera stations at collecting mammal images, and thus could be incorporated into study designs without compromising quality of camera photo data. We suggest a pilot study before large‐scale deployment to evaluate reliability and performance of cameras, particularly when deploying multiple models.

Robust Reinforcement Learning Based Visual Servoing with Convolutional Features

Image-based visual servoing poses a significant challenge for robotic systems, as it involves detecting the object and controlling the robot arm based on image feedback. These tasks are further complicated by various interferences such as changes in ambient lighting, distractions, and background clutter. Recent research suggests that reinforcement learning is a promising approach to learning efficient control policies for such tasks. In this paper, we propose a data-driven approach for closed-loop visual servoing based on a reinforcement learning algorithm that does not require any prior knowledge of the task object or intrinsic camera parameters. Our method utilizes a convolutional neural network for object detection and a servoing strategy that enables the robot to determine the relative camera motion and position the camera at the desired pose. Our experimental results demonstrate that the proposed approach successfully steers the camera using only a single template image of the task object.

Smartphone-based tension disc infiltrometer for soil hydraulic characterisation

The tension disc infiltrometer has become a widespread method to measure soil hydraulic properties under unsaturated conditions. Although several automated devices have been developed, most designs include an elongated water reservoir and a pressure transducer with electronic components to register the water level, increasing its cost and limiting its application. The objective of this work is to present a new tension disc infiltrometer with a compact design of 10 cm diameter and height, where the water level is monitored by a smartphone camera. The infiltration curve is determined from the automated analysis of the images recorded by the smartphone without additional electronic components. The device was first validated in the laboratory by comparing visual measurements (V), the camera imagery (Ca) and a pressure transducer (PT). Next, it was tested on field infiltration experiments. Robust fits (R2 = 0.99) were found between the water level measured with Ca in the laboratory and those obtained with V and PT procedures. The Ca method is accurate, robust and independent of the relative camera position. Good fits were also observed between Ca water level and those obtained with PT in the field experiments. Similar hydraulic conductivity and sorptivity values were obtained with both sensors using the numerical solution of the Haverkamp (NSH) equation. The compact infiltrometer, in conjunction with the smartphone camera, is an accurate, accessible, portable and easy-to-use field-based device for soil hydraulic characterisation.

An unstructured lumigraph based approach to the SVBRDF estimation problem

Appearance preservation aims to estimate reflectance functions to model the way real materials interact with light. These functions are especially useful in digital preservation of heritage and realistic rendering, as they reproduce the appearance of real materials in virtual scenes. This work proposes an image-based process that aims to preserve the appearance of surfaces whose reflectance properties are spatially variant. During image acquisition, this process considers the whole environment as a source of light over the area to be preserved and, assuming the environment is static, it does not require controlled environments. To achieve this goal, the scene geometry and relative camera positions are approximated from a set of HDR images taken inside the real scene, using a combination of structure from motion and multi-view stereo methods. Based on this data, a set of unstructured lumigraphs is traced, on-demand, inside the reconstructed scene. The color information retrieved from these lumigraphs is then used to estimate a linear combination of basis BRDFs for a grid of points in the surface area, defining thus its SVBRDF. This paper details the proposed method and presents the results obtained using real and synthetic settings. It shows that considering the whole environment as a source of light is a viable approach to obtain reliable results and to enable more flexible acquisition setups.

In-situ deflectometric measurement of optical surfaces for precision manufacturing

The in-situ measurement of complex optical surfaces is a challenging task in precision engineering. The phase measuring deflectometry is a powerful measuring method for complex specular surfaces, and it has higher measuring efficiency, stability and dynamic range compared to interferometry. Consequently it is promising to widespread applications in various fields. Deflectometry is essentially a calibration problem, and the measuring accuracy is directly determined by the quality of geometrical calibration. An in-situ deflectometric measuring system is designed based on the single point diamond turning machine. A self-calibration method is developed to specify the relative positions of the camera and screen. Ray tracing is conducted at two positions of an auxiliary reflecting mirror, which is mounted on an air bearing spindle. The accuracy of the geometrical positions can be improved by an order of magnitude by minimizing the deviations of the traced points with respect to the true correspondences. According to the statistical properties of the deviations in reverse ray tracing, the form errors and the position errors can be separated, and the positioning error of the workpiece can be corrected accordingly. Henceforth, the nominal shape of the fabricated workpiece can be fully utilized, and the conventional one-way position-form mapping can be converted into a two-way mapping problem. As for the complex shapes, the whole surface can be covered by sub-aperture measurement. Precise localization of a local region under test is achieved by multi-position imaging, so that correct convergence of the iterative reconstruction process can be guaranteed. Several typical optical surfaces including an off-axis paraboloid mirror are measured, and the measuring accuracy of the proposed method is proved better than 150 nm RMS.

A high-precision online monitoring system for surface settlement imaging of railway subgrade

Abstract In order to improve the measurement accuracy of subgrade settlement deformation system, this study proposes a high-precision, online monitoring system for subgrade settlement imaging. Firstly, the working principle and key technologies of this system are introduced. Secondly, a pose measurement system is designed to solve the problem that the relative position of target and camera in the monitoring system is affected by train vibration, and attitude estimation method p4p(Perspective 4 Points) for rectangular four-feature points target is proposed. Finally, experimental results indicate that when the relative deflection angle is less than 2°, the rotation angle error of pose measurement method in this study is less than 0.09°. The measurement accuracy of this method meets the requirement of this online monitoring system. And on-site testing results illustrate that the monitoring system corrected by the pose measurement system avoids the detection of non-real subgrade settlement and improves the measurement accuracy.

Measurement of Relative Position of Camera and Optical Beacon by Simultaneous Passive Method

The paper deals with a problem of a passive measurement of the relative position of an optical beacon and an optical camera by a simultaneous analytical method. The beacon is composed of nine light sources which are arranged in space in a defined way. The proposed beacon layout enables the measurement of the beacon range and one position angle of the camera. This paper presents the mathematical model of the measurement method and the results, which were gathered on the basis of two experimental measurements. The first experiment was only indicative. The extreme results of the second experiment were as follows: the minimum and maximum absolute percentage errors of the beacon range were zero and 1.72%, the minimum and maximum errors of the position angle were 0.1 deg and 1.64 deg. The standard commercial cameras and lenses with different focal lengths were used.

Flycon

We propose a real-time method for the infrastructure-free estimation of articulated human motion. The approach leverages a swarm of camera-equipped flying robots and jointly optimizes the swarm's and skeletal states, which include the 3D joint positions and a set of bones. Our method allows to track the motion of human subjects, for example an athlete, over long time horizons and long distances, in challenging settings and at large scale, where fixed infrastructure approaches are not applicable. The proposed algorithm uses active infra-red markers, runs in real-time and accurately estimates robot and human pose parameters online without the need for accurately calibrated or stationary mounted cameras. Our method i) estimates a global coordinate frame for the MAV swarm, ii) jointly optimizes the human pose and relative camera positions, and iii) estimates the length of the human bones. The entire swarm is then controlled via a model predictive controller to maximize visibility of the subject from multiple viewpoints even under fast motion such as jumping or jogging. We demonstrate our method in a number of difficult scenarios including capture of long locomotion sequences at the scale of a triplex gym, in non-planar terrain, while climbing and in outdoor scenarios.

Improved Image-Based Localization Using SFM and Modified Coordinate System Transfer

Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database collected from social sharing websites like Flickr or services such as Google Street View. This paper proposes a new method for reliable estimation of the actual query camera location by optimally utilizing structure from motion (SFM) for three-dimensional (3-D) camera position reconstruction, and introducing a new approach for applying a linear transformation between two different 3-D Cartesian coordinate systems. Since the success of SFM hinges on effectively selecting among the multiple retrieved images, we propose an optimization framework to do this using the criterion of the highest intraclass similarity among images returned from retrieval pipeline to increase SFM convergence rate. The selected images along with the query are then used to reconstruct a 3-D scene and find the relative camera positions by employing SFM. In the last processing step, an effective camera coordinate transformation algorithm is introduced to estimate the query's geo-tag. The influence of the number of images involved in SFM on the ultimate position error is investigated by examining the use of three and four dataset images with different solution for calculating the query world coordinates. We have evaluated our proposed method on query images with known accurate ground truth. Experimental results are presented to demonstrate that our method outperforms other reported methods in terms of average error.

Evaluation of the temperature pattern of a complex body from thermal imaging and 3D information: A method and its MATLAB implementation

The standard setting of a camera used in Infrared thermography (IRT) is based on the choice of the same values of emissivity and distance for all pixels of a thermal image even if the emissivity depends on the relative position of camera and observed surface. Often this is not a problem. However, the resulting temperature pattern could be inadequate if a body having a complex shape is observed from strongly constrained positions. In order to face this issue, a procedure aimed at providing a correct temperature pattern by using 3D information related to a point cloud is proposed together with its MATLAB implementation (COMAP3 toolbox). For each pixel of a thermal image, the relative position of camera and observed surface is estimated, leading to pixel-specific values of emissivity and distance. The temperature obtained in this way is also mapped onto the point cloud. The effectiveness of the procedure in recognizing areas characterized by peculiar thermal behavior is shown in the case of a historic cylindrical masonry bell tower (Caorle’s bell tower, Venice, Italy).

Calibration method for 3D gaze tracking systems

In this study, a novel method that calibrates 3D gaze tracking systems using multi-camera global calibration is proposed. In the proposed method, a calibration camera placed in front of the computer screen and the system camera constitute a multi-camera system. Video images are acquired by this multi-camera system to estimate the position of the light sources and the computer screen relative to the system camera. The method first evaluates the transition matrix between the system camera and calibration camera, and then computes the relative position of camera and targets such as light sources and monitor. Finally, it determines the target position in the system camera’s coordinate system. Experiments conducted on a system containing one camera and two light sources verify that the proposed calibration method is accurate. The method overcomes the problem of the screen and light sources not being in view of the system camera. Further, it is relatively simple to apply, is very accurate, and provides a new basis for many other similar calibration systems.

Solution to relative position of cameras without public FOV

Solution to relative position of cameras without public FOV

基于吸热管反射成像法测量抛物槽式太阳能聚光器的面形误差

To improve the concentrate efficiency of the parabolic trough concentrator, the measurement and adjustment of overall surface are needed. The theoretical overlay photographic method is used to measure the installation position and tilt angle of facet mirrors, and then the concentrator surface information is obtained. It can calculate the theoretical shape and position of the receiver in image by measuring the relative position of concentrator and cameras, while the theory position formula of receiver is deduced in concentrator. The experiment of the theoretical overlay photographic method measuring the concentrator surface is done. This method can match the actual position of receiver to theoretical position in image by adjusting installation position of facet mirrors, which verifies the feasibility and correctness of this method.

An Empirical Model of Multiview Video Coding Efficiency for Wireless Multimedia Sensor Networks

We develop an empirical model of the Multiview Video Coding (MVC) performance that can be used to identify and separate situations when MVC is beneficial from cases when its use is detrimental in wireless multimedia sensor networks (WMSN). The model predicts the compression performance of MVC as a function of the correlation between cameras with overlapping fields of view. We define the common sensed area (CSA) between different views, and emphasize that it depends not only on geometrical relationships among the relative positions of different cameras, but also on various object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. With these premises, we first experimentally characterize the relationship between MVC compression gain (with respect to single view video coding) and the CSA between views. Our experiments are based on the H.264 MVC standard, and on a low-complexity estimator of the CSA that can be computed with low inter-node signaling overhead. Then, we propose a compact empirical model of the efficiency of MVC as a function of the CSA between views, and we validate the model with different multiview video sequences. Finally, we show how the model can be applied to typical scenarios in WMSN, i.e., to clustered or multi-hop topologies, and we show a few promising results of its application in the definition of cross-layer clustering and data aggregation procedures.

Self-Calibration of a Binocular Stereo Rig with Varying Intrinsic and Extrinsic Parameters

Self-calibration of stereo rig is essential to many computer vision applications. In this paper, a new self-calibration method is proposed for a binocular stereo rig undergoing a single motion with varying intrinsic and extrinsic parameters. Firstly, we build up a stereo rig model based on the basic platform to describe the transformation of the stereo rig during the motion. Secondly, the characteristics of singular values of the essential matrix are used to estimate the intrinsic parameters of camera. Finally, analyzing the transformation relation between different views, the relative position of cameras and motion of the stereo rig are estimated. Experimental results for both synthetic data and real images are provided to show the performance of the proposed method.

Appendix B

Appendix B

Mitigation of surface reflection in PIV measurements

Surface reflections of high-intensity laser light are a common concern when conducting particle image velocimetry (PIV) measurements. Consequences range from a poor signal-to-noise ratio (overexposure) in near-surface areas up to camera sensor damage. The severity depends on the interplay between three factors: surface properties, laser light intensity and relative camera position. In stereoscopic or tomographic PIV setups, material selection is often the only factor which can be adapted. We present a systematic comparative study, involving different materials and surface treatments. Their potential to mitigate surface reflections is quantified against the reference case of a flat black painted wooden surface. The largest reduction of surface reflection intensity is obtained by applying fluorescent paint on wood or by employing electropolished steel. The more widely used flat black painted wood shows poor behavior.

Surrounding Moving Obstacle Detection for Autonomous Driving Using Stereo Vision

Detection and tracking surrounding moving obstacles such as vehicles and pedestrians are crucial for the safety of mobile robotics and autonomous vehicles. This is especially the case in urban driving scenarios. This paper presents a novel framework for surrounding moving obstacles detection using binocular stereo vision. The contributions of our work are threefold. Firstly, a multiview feature matching scheme is presented for simultaneous stereo correspondence and motion correspondence searching. Secondly, the multiview geometry constraint derived from the relative camera positions in pairs of consecutive stereo views is exploited for surrounding moving obstacles detection. Thirdly, an adaptive particle filter is proposed for tracking of multiple moving obstacles in surrounding areas. Experimental results from real-world driving sequences demonstrate the effectiveness and robustness of the proposed framework.