Vanishing Points Research Articles

The Spectacle of Invisibility: Vanishing Points and the Spatialised Legal Violence of the UK’s Expanding Quasi-Carceral Geography of Immigration Control

ABSTRACT Increasingly, irregularized migrants are pushed to rely on more and more clandestine means in attempts to reach so-called safe havens in Europe and elsewhere. Often, these actions are necessitated by governments instituting ever-more hostile and violent mechanisms of immigration control. Taking as its focus key developments in the UK’s morphing and expanding ‘quasi-carceral geography’ of immigration control, this paper considers some of these developments alongside Derek Gregory’s incisive essay titled analysis of ‘vanishing points’. The paper examines how the spectacular and invisible are instrumentalized simultaneously to produce spatialised legal vanishing points in the UK and beyond by scrutinising three distinct yet interconnected sites where vanishing points materialise, each successively more proximate to UK geographical terrain. Specifically, I examine the multi-scalar legal contestations that surrounded the UK-Rwanda Agreement; the invisibility and tactical ambiguities of juxtaposed border controls in Northern France; and the downgrading of rights, responsibilities, and accountability in amended rules for the accommodation of irregular migrant arrivals, focusing on the former military barracks of Manston, Kent, UK. Drawing on Gregory’s analysis as a framework, the paper traces the significance not only of law’s presence or suspension but also its application, morphing, and contestation within these sites – and more generally – for the emergence and functioning of a continuum of extra-territorial, ambiguous, discretionary, violent spaces where possibilities for sanctuary, safe haven, or security are continually deferred or denied amidst the spectacular invisibility and persistent spatialised legal vanishing points.

A Vehicle Monocular Ranging Method Based on Camera Attitude Estimation and Distance Estimation Networks

A monocular ranging method for forward vehicles in intelligent driving is proposed. This method measures vehicle distance more accurately under the condition of a single camera and can estimate camera attitude in real-time. For the estimation of camera pitch and yaw angles, it is achieved using road vanishing points. The images collected by the camera are sequentially processed through the Roberts operator amplitude calculation, feature point extraction, feature line segment generation, road vanishing point voting, and estimation of camera attitude to obtain pitch and yaw angles. A distance estimation network was designed, which is divided into multiple levels based on image size and incorporates image feature, integrating vehicle grounding point, and vehicle width information, effectively improving ranging accuracy. Finally, validation was conducted on KITTI data, with a relative error (AbsRel) of 8.3%. Additionally, the TuSimple dataset and continuous driving scenarios were also validated, resulting in improved performance compared to previous algorithms.

A new criterion for vanishing points of Widom lines of CO2/natural gas mixture: Application to pipeline and compressor units in CCUS

The Widom lines of mixture indicate the abnormal response behavior of properties of mixed supercritical fluid near the critical point, determining operating conditions in supercritical region of ground equipment in CCUS. Peng-Robinson equation (PR-EOS) is used to calculate Widom lines of isothermal compressibility coefficient, isobaric expansion coefficient, isobaric specific heat, and density of CO2/natural gas quinary mixture. A new criterion is proposed to calculate the vanishing points of Widom lines using the coefficient of variation (CV) of response function distribution, with a clear statistical meaning and an appropriate range, compared with existing criteria. Based on the new criterion, the effect of CO2 concentration on the thermodynamic fluctuations in the near-critical region of CO2/natural gas mixture is analyzed. It is found that thermodynamic fluctuations of mixture are controlled by CO2 concentration and fluctuations can be considered relatively weak when CO2 concentration is below 60 %. The selection and control of operating conditions of pipeline and compressor unit in CCUS application are discussed to cope with the Widom region movement caused by changes in CO2 concentration.

Research on the Vanishing Point Detection Method Based on an Improved Lightweight AlexNet Network for Narrow Waterway Scenarios

When an unmanned surface vehicle (USV) navigates in narrow waterway scenarios, its ability to detect vanishing points accurately and quickly is highly important for safeguarding its navigation safety and realizing automated navigation. We propose a novel approach for detecting vanishing points based on an improved lightweight AlexNet. First, a similarity evaluation calculation method based on image texture features is proposed, by which some scenarios are selected from the filtered Google Street Road Dataset (GSRD). These filtered scenarios, together with the USV Inland Dataset (USVID), compose the training dataset, which is manually labeled according to a non-uniformly distributed grid level. Next, the classical AlexNet was adjusted and optimized by constructing sequential connections of four convolutional layers and four pooling layers and incorporating the Inception A and Inception C structures in the first two convolutional layers. During model training, we formulate vanishing point detection as a classification problem using an output layer with 225 discrete possible vanishing point locations. Finally, we compare and analyze the labeled vanishing point with the detected vanishing point. The experimental results show that the accuracy of our method and the state-of-the-art algorithmic vanishing point detector improves, indicating that our improved lightweight AlexNet can be applied in narrow waterway navigation scenarios and can provide a technical reference for autonomous navigation of USVs.

Semi-supervised vanishing point detection with contrastive learning

Vanishing point (VP) detection in road images plays an important role in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Because it is still a challenging problem to obtain expensive annotated datasets in deep-learning-based supervised training, in this paper, we propose a semi-supervised VP detection method in road images. Our proposed model first extracts high-resolution VP heatmaps of the road images by fusing multi-level and global–local contrastive learning. Then, we apply the π model as a semi-supervised learning framework and feed the whole global network with a small number of training samples to detect VPs. In the experiment, we used Kong’s dataset for the unstructured road test and the KITTI-VP dataset for the structured road test. Compared with the state-of-the-art methods, our model achieved high accuracy and robustness in road VP detection.

VPDETR: End-to-End Vanishing Point DEtection TRansformers

In the field of vanishing point detection, previous works commonly relied on extracting and clustering straight lines or classifying candidate points as vanishing points. This paper proposes a novel end-to-end framework, called VPDETR (Vanishing Point DEtection TRansformer), that views vanishing point detection as a set prediction problem, applicable to both Manhattan and non-Manhattan world datasets. By using the positional embedding of anchor points as queries in Transformer decoders and dynamically updating them layer by layer, our method is able to directly input images and output their vanishing points without the need for explicit straight line extraction and candidate points sampling. Additionally, we introduce an orthogonal loss and a cross-prediction loss to improve accuracy on the Manhattan world datasets. Experimental results demonstrate that VPDETR achieves competitive performance compared to state-of-the-art methods, without requiring post-processing.

On-orbit calibration of space camera lens distortion using a single image

Since space cameras need to withstand the harsh mechanical and thermal conditions in the space environment for a long time, it is necessary to calibrate them in orbit. However, existing calibration methods have various disadvantages, making them impossible to use in orbit. To address this problem, we present an on-orbit calibration of space camera lens distortion with the vanishing points obtained from a single image of the solar panel. First, we propose a parallel-line-extraction method based on collinear constraints to obtain the parallel lines. Then, we train the optimal vanishing point using the common point constraint method. Using the optimal vanishing point, we establish the optimization function of lens distortion based on vanishing point consistency. Finally, we present an improved genetic algorithm to solve the optimization function. Simulations and experiments show that the proposed method is flexible and robust.

Illustrating Tomorrow: The Role of Comics in Engaging Research Participants in Disruptive Futures Research

This study examines the application of comics in futures research to involve stakeholders in infrastructure strategy discussions for disruptive futures. It employs a graphic novel called “Vanishing Point! Our Built World,” which augments generative AI art with human storytelling to transport participants into a speculative 2150 with a solarpunk theme. The narrative applies the concept of future-self continuity and a temporal discontinuity framework to promote visions of sustainability for both humanity and the environment. The research emphasises the ability of comics to foster meaningful discussions on future infrastructure coordination by incorporating Social Construction Theory, Social Construction of Technology Theory, and Actor Network Theory. Comics being adapted into videos and used in workshops highlight their effectiveness in promoting engagement and facilitating semi-structured discussions. This demonstrates comics as a dynamic tool in futures research and participatory methodologies. Relevant themes: Anthropocene, Ecocene and Climate Change/Post-capitalism/Utopias, Eutopias and Dystopias/Solar Punk and its relations/Artificial Intelligence and Automation/Wicked Problems/Futures and Metaphor/Reimagining Futures Tools and Methodologies. Statement acknowledging use of Generative AI Art and copyright ownership: “Vanishing Point! Our Built World” graphic novel artwork was created with Midjourney generative AI. These images were created using the author’s prompts and ideas. The author owns these AI-generated photos and their derivatives. Synthesia was used to create animated films and AI-generated avatars from comic artwork. Statement on the use of Generative AI in the production of this manuscript: Referring to Author Guidelines on Using Generative AI and Large Language Models| Sage Learning Resources (sagepub.com ), this manuscript employed various AI tools to assist with research and writing. ChatGPT4 and its plugin, Scholar AI, helped identify relevant literature in addition to traditional research. Litmaps revealed article linkages, clarifying the research landscape. ChatGPT4 critiqued the manuscript. Quillbot improved the manuscript's clarity and consistency by improving its language.

VANISHING POINT AIDED LANE DETECTION USING A MULTI-SENSOR SYSTEM

Abstract. Lane Detection is a critical component of an autonomous driving system that can be integrated alongside with High-definition (HD) map to improve accuracy and reliability of the system. Typically, lane detection is achieved using computer vision algorithms such as edge detection and Hough transform, deep learning-based algorithms, or motion-based algorithms to detect and track the lanes on the road. However, these approaches can contain incorrectly detected line segments with outliers. To address these issues, we proposed a vanishing point aided lane detection method that utilizes both camera and LiDAR sensors, and then employs a RANSAC-based post-processing method to remove potential outliers to improve the accuracy of the detected lanes. We evaluated this method on four datasets provided from the KITTI Benchmark Suite and achieved a total precision of 87%.

Efficient Vanishing Point Estimation for Accurate Camera Rotation Estimation in Indoor Environments

Efficient Vanishing Point Estimation for Accurate Camera Rotation Estimation in Indoor Environments

Real-time risk assessment of road vehicles based on inverse perspective mapping

Pan/Tilt/Zoom (PTZ) cameras play an important role in traffic scenes due to their wide monitoring fields and high flexibility. However, since the focal length and angle of PTZ cameras change irregularly with the monitoring needs, it is difficult to obtain accurate physical information about the real world from the image information of PTZ cameras. Aiming to address the need for real-time risk assessment of road vehicles in traffic monitoring scenarios, a vehicle position and velocity measurement scheme based on camera inverse perspective transformation is proposed, along with a method for real-time risk assessment based on the position and velocity. Specifically, Firstly, the vehicle target in the video is detected and tracked by deep learning YOLO detection algorithm and optical flow tracking algorithm. According to the obtained trajectory set, the vanishing points in the road direction are calculated by Cascade Hough Transform and the road marking lines are detected. Then, according to the vanishing point and marking line, the camera calibration task is accomplished via exploratory focal length. After camera calibration, the camera-to-road inverse perspective transformation is applied to project the image plane onto the road surface and obtain, the actual position information of vehicles. Finally, the vehicle speed measurement and real-time road risk assessment are achieved by calculating the average of instantaneous velocities across multiple frames. Simulation experiment results in a traffic monitoring scenario demonstrate that this perspective-based method for real-time road vehicle risk assessment achieves good stability and practicality, which meets the requirements for vehicle speed measurement and real-time road risk assessment.

A calibration method for line-structured light using mirror-based virtual binocular vision system

A calibration method for line-structured light (LSL) by using a virtual binocular vision system (VBVS) composed of one camera and a front coating plane mirror is promoted in this work. The front coating plane in the VBVS can generate much less coplanarity error in lithographic feature points and remarkably decline the imaging distortion during back coating. An encoded target is proposed to distinguish between real corners and virtual corners (mirrored corners) and achieve high-precision matching between real and virtual corners when the target is occluded during the VBVS calibration. A parameter optimization method based on 3D constraints is presented in the work to obtain accurate structural parameters and thus guarantee precise reconstruction of the LSL. Moreover, the laser stripe and its mirrored image meet the auto-epipolar constraint. Therefore, the matching between the real and virtual stripes can be realized based on the vanish point. The performance of our method is verified in the experiments.

Automatic Calibration for Monocular Cameras in Highway Scenes via Vehicle Vanishing Point Detection

Automatic camera calibration is a fundamental technology for 3D traffic parameter extraction. With the popularity of pan-tilt-zoom cameras, this technique demonstrates great potential to enhance traffic safety and efficiency, especially for highways. This paper aims to present a fully automatic calibration method for surveillance cameras in highway scenes. Our system is divided into two stages. In the first stage, a deep convolution neural network was used to estimate a pair of orthogonal vanishing points from multiple vehicles. This process transformed vanishing point detection into an estimation of vehicle direction, which was further determined by introducing the central residual mechanism. In the diamond space, the straight lines formed by these directions accumulated the final positions of the vanishing points. More importantly, we proposed a novel algorithm for estimating the lane width using vehicle trajectories in the second stage. It can be used to calculate the camera height, making the calibration fully automated. We also corrected the distorted lens using vehicle trajectories. Comprehensive experiments were conducted on the proposed data set and the BoxCars116k data set. The results indicate that the composite mechanism (i.e., classification and central residual) significantly improves the accuracy and robustness of the vanishing point estimation. Combined with automatic camera height estimation, our technology is superior to the most representative methods in calibration performance. Since it does not have any constraints on road geometry and camera placement, our approach applies to most highway surveillance systems.

Calibration of central catadioptric cameras based on the projection properties between the right and oblique cones.

Central catadioptric cameras with a single effective viewpoint can easily transform the original image into a perspective image. In this study, we proposed a calibration algorithm for central catadioptric cameras. A vanishing line was recovered by the images of two sphere images (or line images) and mirror contour based on the properties of right and oblique cones. Subsequently, the intrinsic parameters were solved by the image of circular points and the orthogonal vanishing points. Finally, the distortion coefficient was estimated based on the geometric properties of the right cone. The simulation and real-time experiments conducted proved the effectiveness and feasibility of the proposed algorithm.

The Point of Vanishing: Pater’s Loss of Perspective

This article addresses the importance of perspective in the work of Walter Pater by putting his treatment of the topic in Plato and Platonism and The Renaissance in relation to its place in art history and to the work of some of Pater’s immediate Victorian predecessors. The classic formulations of Erwin Panofsky prove useful in identifying the gains and losses to the perceiving self entailed in the evocation of perspective in relation to a mutable world.

IMPLEMENTATION OF THE SMARTPHONE CAMERA IN THE MEASURES OF NARROW STREET FACADES

This paper aims to perform metric measurements of narrow street façades using single image captured by smartphone’s camera. Since tight area accompanied by narrow street limits object to camera distance, object lines perpendicular to façade do not appear in image and consequently their vanishing point (VP) is hard to detect. Accordingly, semi-automated MATLAB® application was designed depending only on two orthogonal VPs. Novelty of work comes from using smartphone as a cost and time efficient tool for measurements, depending only on two VPs, and applying image line refinement approach exploiting detected VPs. Three single images were captured by three different smartphones. Then, undistorted single images were formed after calibrating cameras. Image lines for horizontal and vertical object lines were extracted semi-automatically. Two VPs were detected applying two models: Model-I solves for vanishing points’ Cartesian coordinates, whereas Model-II solves for angle coordinate peaks of histogram. Image line refinement approach was applied before applying cross-ratio using one horizontal and one vertical reference lines to calculate object lengths of 46 check lines (horizontal and vertical). Proposed models provided reliable and comparable results. Applying line refinement approach improved solution with best overall accuracy of 0.010 m and 0.011 m for Model-I and Model-II, respectively.

A fast vanishing point detection method based on row space features suitable for real driving scenarios

The vanishing point (VP) is particularly important road information, which provides an important judgment criterion for the autonomous driving system. Existing vanishing point detection methods lack speed and accuracy when dealing with real road environments. This paper proposes a fast vanishing point detection method based on row space features. By analyzing the row space features, clustering candidates for similar vanishing points in the row space are performed, and then motion vectors are screened for the vanishing points in the candidate lines. The experimental results show that the average error of the normalized Euclidean distance is 0.0023716 in driving scenes under various lighting conditions. The unique candidate row space greatly reduces the amount of calculation, making the real-time FPS up to 86. It can be concluded that the fast vanishing point detection proposed in this paper would be suitable for high-speed driving scenarios.

Zebra-crossing detection based on cascaded Hough transform principle and vanishing point characteristics

Abstract In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.

Vulnerable underground entrance understanding for visual surveillance systems

Protecting critical infrastructure through visual surveillance is of vital importance, especially in underground entrance environments where large chunks of sloping glass ceilings are particularly susceptible to various types of terrorist activity. However, owing to the diversity of underground entrance environments, understanding them remains a challenge. Traditional 3D layout and object pose estimation that are evaluated on 3D point clouds or RGB-D data are energy-consuming and difficult to account for semantic information in environments. In this study, we present a methodology to understand underground entrance environments, and to recover their 3D reconstruction from a monocular camera. Clusters of sloping angle projections are extracted. Through their corresponding vanishing points (VPs), surfaces of sloping structures are estimated. Relative geometric constraints of different planes are built to bridge the gap between 2D sloping surfaces and 3D reconstruction without precise depth or point clouds. An underground entrance scene is approximated by Manhattan and sloping non-Manhattan structures in 3D reconstruction. The approach requires no prior training, and it requires neither the camera being calibrated nor the camera internal parameters being constant. Compared to the ground truth, the percentage of incorrectly understood pixels were measured and the results demonstrated that the method can successfully understand underground entrance scenes, meeting the requirements in safety monitoring for critical infrastructures from a resource-constrained surveillance camera.

Closed-Form Solution of Principal Line for Camera Calibration Based on Orthogonal Vanishing Points

Vanishing point is an important geometric element in sports video. In this paper, a new calibration algorithm is proposed by using the algebraic and geometric properties of vanishing points, which resolves the three main problems of the traditional camera calibration technology based on vanishing points: (1) calibrating camera with varied focal length; (2) screening out outliers from a set of images; (3) estimation of distortion coefficients. The principal line passes through the principal point, and the algebraic relationship between it and the vanishing points is deduced. Using the geometry feature of the principal line, problems (1) and (2) can be easily solved. The linear relationship between the point and the line is used to estimate the distortion coefficient under the condition of obtaining the principal point. Simulation and real experiments show the validity and robustness of the proposed algorithm, and satisfactory results can be obtained by solving the above three problems.