Camera Tilt Angle Research Articles

The method for evaluating the coverage rate of roadside monitoring cameras in road areas

Accurately assessing the coverage rate of roadside monitoring cameras in road areas is crucial for intelligent transportation systems and road risk assessment. This paper presents a new method that uses Carsim software to simulate different camera tilt angles and road geometry conditions to evaluate the coverage area of roadside monitoring cameras. By adjusting the camera tilt angles and the curve radius of the road, the coverage effect under different conditions is analyzed, and affine transformation is used to calculate the actual coverage area. The research results show that this method can effectively fit the road area, providing theoretical support for the deployment of monitoring cameras in traffic.

Real-time Self-calibration Method of Vehicle Camera Attitude Based on ANN

The research of the monocular vision ranging method has great research value in the field of intelligent vehicles. Camera calibration is an important step in a monocular vision ranging model. The accuracy of camera calibration directly determines the accuracy of ranging. Because the camera attitude may change during vehicle driving, it will have a certain impact on the accuracy of the ranging algorithm. Therefore, based on a monocular ranging model and focal length calibration method based on geometric transformation, a real-time camera attitude self-calibration method based on the width measurement model and ANN model is proposed in this paper. Firstly, the method takes the wheel width of the known vehicle type as a fixed value, takes the contact points between the two wheels of the front vehicle and the ground as the feature points, and brings them into the width measurement model to form constraints. However, the single formula formed by a single pair of feature points cannot solve the camera inclination of multiple dimensions at the same time, and it is difficult to solve the analytical solution of the complex formula. In this paper, the ANN model is proposed to fit and solve the formula, and a large number of data sets are obtained through experimental point distribution to train and test the model. The results show that the ranging accuracy of using this method to calibrate the camera tilt angle is unchanged compared to the previous measurement accuracy using sensors, meeting the ranging requirements

Optimization of UAVs‐SfM data collection in aeolian landform morphodynamics: a case study from the Gonghe Basin, China

AbstractUAVs‐SfM (unmanned aerial vehicles‐structure‐from‐motion) systems can generate high‐resolution three‐dimensional (3D) topographic models of aeolian landforms. To explore the optimization of UAVs‐SfM for use in aeolian landform morphodynamics, this study tested flight parameters for two contrasting aeolian landform areas (free dune and blowout) to assess the 3D reconstruction accuracy of the UAV survey compared with field point measurements using differential RTK‐GPS (real‐time kinematic‐global positioning system). The results reveal the optimum UAVs‐SfM flight set‐up at the free‐dune site was: flying height = 74 m, camera tilt angle = −90°, photograph overlap ratio = 85%/70% (heading/sideways). The horizontal/vertical location error was around 0.028–0.055 m and 0.053–0.069 m, respectively, and a point cloud density of 463/m3 was found to generate a clear texture using these flying parameters. For the < 20 m deep blowout the optimum set‐up with highest accuracy and the lowest cliff texture distortion was: flying height = 74 m combined camera tilt angle = −90° and −60°, photograph overlap ratio = 85%/70% (heading/sideways), and an evenly distributed GCPs (ground control points) density of 42/km2 using these flying parameters. When the depth of the blowouts exceeded 40 m, the optimum flight/survey parameters changed slightly to account for more challenging cliff texture generation: flying height = 80 m (with −90° and −60°combined camera tilt angle), GCPs density = 63/km2 to generate horizontal and vertical location error of 0.024 m and 0.050 m, respectively, and point cloud density of 2597.11/m3. The main external factors that affect the successful 3D reconstruction of aeolian landforms using UAVs‐SfM are the weather conditions, manipulation errors, and instrument system errors. The UAVs‐SfM topographic monitoring results demonstrate that UAVs provide a viable and robust means for aeolian landform morphodynamics monitoring. Importantly, the rapid and high precision 3D reconstruction processes were significantly advanced using the optimal flight parameters reported here. © 2020 John Wiley & Sons, Ltd.

Enhancing UAV–SfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images

Complex landscapes with high topographic relief and intricate geometry present challenges for complete and accurate mapping of both lateral (x, y) and vertical (z) detail without deformation. Although small uninhabited/unmanned aerial vehicles (UAVs) paired with structure-from-motion (SfM) image processing has recently emerged as a popular solution for a range of mapping applications, common image acquisition and processing strategies can result in surface deformation along steep slopes within complex terrain. Incorporation of oblique (off-nadir) images into the UAV–SfM workflow has been shown to reduce systematic errors within resulting models, but there has been no consensus or documentation substantiating use of particular imaging angles. To address these limitations, we examined UAV–SfM models produced from image sets collected with various imaging angles (0–35°) within a high-relief ‘badland’ landscape and compared resulting surfaces with a reference dataset from a terrestrial laser scanner (TLS). More than 150 UAV–SfM scenarios were quantitatively evaluated to assess the effects of camera tilt angle, overlap, and imaging configuration on the precision and accuracy of the reconstructed terrain. Results indicate that imaging angle has a profound impact on accuracy and precision for data acquisition with a single camera angle in topographically complex scenes. Results also confirm previous findings that supplementing nadir image blocks with oblique images in the UAV–SfM workflow consistently improves spatial accuracy and precision and reduces data gaps and systematic errors in the final point cloud. Subtle differences among various oblique camera angles and imaging patterns suggest that higher overlap and higher oblique camera angles (20–35°) increased precision and accuracy by nearly 50% relative to nadir-only image blocks. We conclude by presenting four recommendations for incorporating oblique images and adapting flight parameters to enhance 3D mapping applications with UAV–SfM in high-relief terrain.

Analyze Angle of the Camera Payload LSU-02 in Mission of Aerial Photo

In carrying out its mission, LAPAN Surveillance UAV version 2 (LSU-02) was equipped with a camera payload sensor mounted on a gimbal system. When taking upright aerial photos according to photogrammetric requirements, the axis of the aerial camera must be aligned with the direction of gravity with a slope tolerance less than 3 degrees (<3°), so that the shooting results meet the map to the desired standard. The camera payload was equipped with a 3-axis gimbal system which pitch and roll directions installed by the gyro sensor to measure the camera's tilt angle. Gimbal camera payload with dimensions (160×170×155) mm are designed to be installed on a casing (190×190×180) mm according to the available space in the LSU-02 payload space, which was the length x width x height (190×190×180) mm. Testing was done by flying LSU-02 on Rumpin and Pamengpeuk runway. From the test results, it was obtained the deviation of roll and pitch angle less than 3degree, which the gimbal angle oscillation when the payload camera shooting was only ± 1 degree with the respon time until it reaches a stable condition of approximately 35 to 55 seconds, and the offset value for roll and pitch approximately -1 degree, which it has met the standard for making aerial photo maps according to photogrammetric requirements.

Perceptual evaluation of maneuvering motion illusion for virtual pedestrians

Crowd simulations span a wide spectrum of application domains, most notably video games, evacuation scenarios, and the movie industry. However, it is not obligatory that all virtual populace applications have the primary objective of realistic simulation. In most instances, it is necessary and sufficient that viewers perceive the crowd as plausible. Even for a crowd consisting of agents navigating on linear trajectories without any maneuvers, visual motion illusion elicited by these trajectories might appear to be a natural consequence, causing them to be perceived as wriggling rather than straight. In this respect, we evaluate in this study whether simulated 3D human agents walking with constant, collision-free velocities, induce such a maneuvering motion illusion, aiming toward an efficient real-time crowd simulation. For this purpose, we recorded videos of virtual human crowds with different parameter combinations, such as the agent walking speed, crowd density, camera tilt angle, and camera distance. These videos were watched by human subjects who were instructed to mark the virtual agents who they thought had changed their gait directions. The analyzed results revealed that participants claimed the presence of maneuvering virtual agents in the videos, even though there were none in any of them. Spatial grouping of the markings highlighted that the participants mainly focused on the central area of the simulation environment, and spatiotemporal analysis of the click data also showed stronger evidence to such an illusion (see accompanying video). Furthermore, we found that all of the referred parameters have statistically significant main effects on the number of marked agents per watched video.

Integration of aerial oblique imagery and terrestrial imagery for optimized 3D modeling in urban areas

Photorealistic three-dimensional (3D) models are fundamental to the spatial data infrastructure of a digital city, and have numerous potential applications in areas such as urban planning, urban management, urban monitoring, and urban environmental studies. Recent developments in aerial oblique photogrammetry based on aircraft or unmanned aerial vehicles (UAVs) offer promising techniques for 3D modeling. However, 3D models generated from aerial oblique imagery in urban areas with densely distributed high-rise buildings may show geometric defects and blurred textures, especially on building façades, due to problems such as occlusion and large camera tilt angles. Meanwhile, mobile mapping systems (MMSs) can capture terrestrial images of close-range objects from a complementary view on the ground at a high level of detail, but do not offer full coverage. The integration of aerial oblique imagery with terrestrial imagery offers promising opportunities to optimize 3D modeling in urban areas. This paper presents a novel method of integrating these two image types through automatic feature matching and combined bundle adjustment between them, and based on the integrated results to optimize the geometry and texture of the 3D models generated from aerial oblique imagery. Experimental analyses were conducted on two datasets of aerial and terrestrial images collected in Dortmund, Germany and in Hong Kong. The results indicate that the proposed approach effectively integrates images from the two platforms and thereby improves 3D modeling in urban areas.

Ways to increase precision and accuracy of wound area measurement using smart devices: Advanced app Planimator.

IntroductionWound surface area measurement is important as therapeutic decisions may depend on the change of wound surface area over time. Digital planimetry is a popular technique in wound area measurement. It is accurate and repeatable when calibration is performed with 2 rulers placed at opposite sides of a wound. The aim of the current study was improving accuracy and precision of wound area measurement using capabilities of smart devices.MethodsThe correction of area measurement based on calculated camera tilt angle and the calculation of calibration coefficient of linear dimensions as weighted average were proposed. These and other improvements were applied in the Planimator app for Android, which was then used in the study. Accuracy and precision of the Planimator app were compared to the Visitrak device, the SilhouetteMobile device, the AreaMe software, and to the digital planimetry based on 2-ruler calibration with pictures taken by the smartphone, compact, and D-SLR cameras. Areas of 40 wound shapes of area ranged from 0.14 to 31.72 cm2 were measured with each device. Medians of relative errors (REs) were compared in the accuracy tests and standard deviations (SDs) of relative differences (RDs) were compared in the tests of precision.ResultsThe median of REs for the Planimator app was not significantly different from the medians of REs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the medians of REs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The SD of RDs for the Planimator app was not significantly different from the SDs of RDs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the SDs of RDs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The Planimator app installed at a smartphone revealed to be 2-fold more accurate and 1.5-fold more precise than the measurements with using ImageJ software based on pictures taken with the same smartphone.ConclusionsThe Planimator app occurred to have the same accuracy and precision as measurements with digital planimetry with 2-ruler calibration and based on pictures from a compact camera or a D-SLR camera. This app showed better accuracy and precision than the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera.

Experimental validation of cost-effective vision-based structural health monitoring

Monitoring structural displacement responses can provide quantitative information for both structural safety evaluations and maintenance purposes. To overcome the limitations of conventional displacement sensors, advanced noncontact vision-based systems offer a promising alternative. This study validates the potentials of the vision displacement sensor for cost-effective structural health monitoring. The results of laboratory experiments on simply-supported beam structures demonstrate the high accuracy of the vision sensor for dense full-field displacement measurements. The identified natural frequencies and mode shapes from measurements by using one camera match well with those from an array of accelerometers. Moreover, the smoother mode shapes make possible the noncontact damage detection based on the conventional mode shape curvature index. This study also discusses the issues concerning the practical applications of the vision displacement sensors, such as the scaling factor determination, measurement with small camera tilt angles, tradeoffs between the measurement resolution and measurement points or field of view, etc. Furthermore, the remote, real-time and multi-point measurement capacities of the vision sensor are confirmed through field tests of Manhattan Bridge during train passing.

Fast Orientation of Video Images of Buildings Acquired from a UAV without Stabilization.

The aim of this research was to assess the possibility of conducting an absolute orientation procedure for video imagery, in which the external orientation for the first image was typical for aerial photogrammetry whereas the external orientation of the second was typical for terrestrial photogrammetry. Starting from the collinearity equations, assuming that the camera tilt angle is equal to 90°, a simplified mathematical model is proposed. The proposed method can be used to determine the X, Y, Z coordinates of points based on a set of collinearity equations of a pair of images. The use of simplified collinearity equations can considerably shorten the processing tine of image data from Unmanned Aerial Vehicles (UAVs), especially in low cost systems. The conducted experiments have shown that it is possible to carry out a complete photogrammetric project of an architectural structure using a camera tilted 85°–90° ( or ω) and simplified collinearity equations. It is also concluded that there is a correlation between the speed of the UAV and the discrepancy between the established and actual camera tilt angles.

Влияние углов наклона съемочной камеры на пространственное разрешение космических снимков

Влияние углов наклона съемочной камеры на пространственное разрешение космических снимков

規範フロー場と実フロー場の差異解析による路上障害物の検出と危険度提示に関する研究

Many researches have been done as for outdoor scene recognition by analyzing optical flow information. However, a practical driver-oriented system has not been proposed yet that notices the “Risk degree” when multiple obstacles exist, such as, parking cars and pedestrians. This article presents a new method to detect stand still obstacles using the “model flow” corresponding to optical flow field where there is no obstacle. The model flow is calculated using the knowledge as for navigation environments. Standard Points which are regularly set on image coordinates are transformed to Environment Points on road plane using several parameters such as, steering angle and camera tilt angle. Stand still objects are automatically detected by analyzing the difference between a model flow field and a real optical flow field which is acquired by an in-vehicle camera image. The algorithms have been installed in a desktop PC by Microsoft Visual C++6.0 programs. Experimental results by several kinds of outdoor scenes have shown the effectiveness of the proposed method. Total detection ratio is 91.0% and that in Dangerous case is 100%.

Factors contributing to guidance performance when using a camera-based guidance aid.

A guidance aid is a device that provides guidance information to the driver rather than replacing the driver. With a camera-based guidance aid, the view seen by a forward-looking video camera is displayed on a monitor situated within the operator station of the vehicle. As the vehicle moves forward, images of the ground scroll vertically across the monitor. The rate at which the image scrolls, the image velocity, is related to the forward velocity of the vehicle, the placement of the camera (height and tilt angle), and the optical characteristics of the guidance camera. When tested with a tractor at forward velocities between 1.6 and 12.8 km/h, lateral error increased linearly as image velocity increased. Driver self-confidence decreased linearly as image velocity increased. Based on subjective feedback, drivers preferred a camera tilt angle of 20 degrees (over either 30 degrees or 40 degrees) because it yielded the greatest look-ahead distance. Statistically, a tilt angle of 30 degrees was best for a camera with a narrow field of view (narrow FOV, 20 degrees in the lateral direction). For a camera with a wide field of view (wide FOV, 39 degrees in the lateral direction), there was no statistical difference. For the narrow FOV camera, a camera height of 1.1 m yielded statistically smaller lateral errors than a camera height of 1.5 m. There was no statistical difference for the wide FOV camera. Overall, the lateral error was statistically smaller for the narrow FOV camera than for the wide FOV camera due to the difference in the lateral ratio for each camera, where the lateral ratio is the ratio of the lateral field of view of the camera to the fixed monitor width.

A VISION BASED SYSTEM FOR OBJECT DETECTION IN UNDERWATER IMAGES

In this paper, a vision-based system for underwater object detection is presented. The system is able to detect automatically a pipeline placed on the sea bottom, and some objects, e.g. trestles and anodes, placed in its neighborhoods. A color compensation procedure has been introduced in order to reduce problems connected with the light attenuation in the water. Artificial neural networks are then applied in order to classify in real-time the pixels of the input image into different classes, corresponding e.g. to different objects present in the observed scene. Geometric reasoning is applied to reduce the detection of false objects and to improve the accuracy of true detected objects. The results on real underwater images representing a pipeline structure in different scenarios are shown. The presence of seaweed and sand, different illumination conditions and water depth, different pipeline diameter and small variations of the camera tilt angle are considered to evaluate the algorithm performances.