Browse Topic: Photogrammetry
Testing aircraft antennas is challenging since optimal tests are made after antenna installation. Aircraft are often taken to anechoic antenna test facilities which create long lead times, transportation hassle, and very high costs. Portable alternatives exist but often have compromised testing fidelity. Innovators at the NASA Glenn Research Center have developed the PLGRM system, which allows an installed antenna to be characterized in an aircraft hangar. All PLGRM components can be packed onto pallets, shipped, and easily operated.
The 3D crush model can be obtained by any suitable photogrammetry method using this image set and is intended to graphically represent in photographs the shape and orientation of the damaged surface(s) relative to the undamaged, or least damaged, portion of the vehicle. The procedure is intended to provide an image set sufficient to determine, with the use of photogrammetric methodologies, the 3D location of points on the crushed surface of the damaged vehicle. Measurement of the exterior damaged surface(s) on a vehicle is a necessary step in quantifying the deformation caused by a collision and the energy dissipated by the deformation process. The energy analysis is sometimes called a crush analysis. Evaluation of the energy dissipated is useful in reconstructing the change in the velocity of the vehicles (delta-V) involved in a collision. This guideline is intended for use by investigators who do not have photogrammetry expertise, special equipment or training and may be constrained
A new spatial calibration procedure has been introduced for infrared optical systems developed for cases where camera systems are required to be focused at distances beyond 100 meters. Army Combat Capabilities Development Command Armaments Center, Picatinny Arsenal, NJ All commercially available camera systems have lenses (and internal geometries) that cannot perfectly refract light waves and refocus them onto a two-dimensional (2D) image sensor. This means that all digital images contain elements of distortion and thus are not a true representation of the real world. Expensive high-fidelity lenses may have little measurable distortion, but if sufficient distortion is present, it will adversely affect photogrammetric measurements made from the images produced by these systems. This is true regardless of the type of camera system, whether it be a daylight camera, infrared (IR) camera, or camera sensitive to another part of the electromagnetic spectrum. The most common examples of large
All commercially available camera systems have lenses (and internal geometries) that cannot perfectly refract light waves and refocus them onto a two-dimensional (2D) image sensor. This means that all digital images contain elements of distortion and thus are not a true representation of the real world. Expensive high-fidelity lenses may have little measurable distortion, but if sufficient distortion is present, it will adversely affect photogrammetric measurements made from the images produced by these systems. This is true regardless of the type of camera system, whether it be a daylight camera, infrared (IR) camera, or camera sensitive to another part of the electromagnetic spectrum.
NASA researchers have developed a compact, cost-effective imaging system using a co-linear, high-intensity LED illumination unit to minimize window reflections for background-oriented schlieren (BOS) and machine vision measurements. The imaging system tested in NASA wind tunnels can reduce or eliminate shadows that occur when using many existing BOS and photogrammetric measurement systems; these shadows occur in existing systems for a variety of reasons, including the severe back-reflections from wind tunnel viewing port windows and variations in the refractive index of the imaged volume.
The world is going through the fourth industrial revolution, where digital transformation is one of the global market trends. To maintain competitive advantages and sustainable businesses, an increasing number of companies and organizations are embracing digital transformation processes. These organizations are changing their business and processes and creating new business models with the help of digital technologies. Taking all industries and business models to unprecedented heights and in a certain way consolidating globalization. For such digital transformation, technologies like IoT (Internet of Things), artificial intelligence, machine learning, neural networks, and others are increasingly common. This paper seeks to define what technical aspects are involved to implement digitalization in the process of vehicle collision data analysis. In this sense, insurance companies are aware of the changes and are trying to follow the trends and updating themselves to provide better
Traffic cameras, dash-cameras, surveillance cameras, and other video sources increasingly capture critical evidence used in the accident reconstruction process. The iNPUT-ACE Camera Match Overlay tool can utilize photogrammetry to project a two-dimensional video onto three-dimensional point cloud software to enable measurements to be directly taken from the video. Those measurements are commonly used, and critical for, the determination of vehicle speed in accident reconstruction. The accuracy of the Camera Match Overlay tool has not yet been thoroughly examined. To validate the use of the tool to measure vehicle speed for accident reconstruction, data were collected from a series of tests involving three traffic cameras, a stationary and moving dash-camera, a stationary and moving cell-phone camera, and a doorbell surveillance camera. Each camera provided unique specifications of quality and focal length to ensure the tool would be tested in a variety of scenarios. Vehicles drove past
This paper introduces a method for calculating vehicle speed and uncertainty range in speed from video footage. The method considers uncertainty in two areas; the uncertainty in locating the vehicle’s position and the uncertainty in time interval between them. An abacus style timing light was built to determine the frame time and uncertainty of time between frames of three different cameras. The first camera had a constant frame rate, the second camera had minor frame rate variability and the third had more significant frame rate variability. Video of an instrumented vehicle traveling at different, but known, speeds was recorded by all three cameras. Photogrammetry was conducted to determine a best fit for the vehicle positions. Deviation from that best fit position that still produced an acceptable range was also explored. Video metadata reported by iNPUT-ACE and Mediainfo was incorporated into the study. When photogrammetry was used to determine a vehicle’s position and speed from
Forensic disciplines are called upon to locate evidence from a single camera or static video camera, and both the angle of incidence and resolution can limit the accuracy of single image photogrammetry. This research compares a baseline of known 3D data points representing evidence locations to evidence locations determined through single image photogrammetry and evaluates the effect that object resolution (measured in pixels), and angle of incidence has on accuracy. Solutions achieved using an automated process where a camera match alignment is calculated from common points in the 2D imagery and the 3D environment, were compared to solutions achieved in a more manual method by iteratively adjusting the camera’s position, orientation, and field-of-view until an alignment is achieved. This research independently utilizes both methods to achieve photogrammetry solutions and to locate objects within a 3D environment. Results are compared for a greater understanding of the accuracies that
Photogrammetry is a commonly used and accepted technique within the field of accident reconstruction for taking measurements from photographs. Previous work has shown the accuracy of optimized close-range photogrammetry techniques to be within 2 mm compared to other high accuracy measurement techniques when using a known calibrated camera. This research focuses on the use of inverse camera close-range photogrammetry, where photographs from an unknown camera are used to model a vehicle. Photogrammetry is a measurement technique that utilizes triangulation to take measurements from photographs. The measurements are dependent on the geometry of the camera, such as the sensor size, focal length, lens type, etc. Three types of cameras were tested for accuracy; a high-end commercial camera, a point and shoot camera, and a cell phone camera. This study indicates that in a properly conducted inverse photogrammetry project, an analyst can be 95% confident the true position of a point will be
Feasibility in Manufacturing of autonomous unmanned aerial vehicles at low cost allows the UAV developers to bring it out with numerous applications for society. Civil domain is a widely developing platform which initiated the development of UAV for civilian applications like bridge inspection, building monitoring, life or strength estimation of historical places and also outdoor and indoor mapping of buildings. These autonomous UAVs with high resolution camera fly over and around the construction sites, buildings, mines and captures images of various locations and point clouds in all sides of the building and creates a 3D map by using photogrammetry techniques. The software auto generates the report and updates it to the cloud which can be accessed online. Autonomous operations are quite difficult in new environments which requires SLAM (simultaneous localization and mapping) to operate the UAV between open spaces. This paper describes the technique of mapping a construction site
In this paper will be explained how photogrammetry and tracking technologies are a highly accurate alternative to accelerometers instrumented sensors related to distances calculations between objects or vehicle interior parts and the dummies. Photogrammetry is used to calculate the real-world point’s position on an image. The tracking system uses algorithms to follow points and keep the same center point at each movie frame. A software application combines these two elements to provide position, velocity, acceleration and angles of every point on the movie for the 3-dimensional axis. The tracking technology can be applied for on dummy’s analysis head impact criterion (HIC) against internal structure and objects as the pole. The use of internal sensors for this kind of analysis, only offers a yes/no response and yet tracking provides the exact distance between head and the interior components. Using tracking technology the distance between the dummy’s head and any other structural part
In an accident reconstruction, vehicle speeds and positions are always of interest. When provided with scene photographs or fixed-location video surveillance footage of the crash itself, close-range photogrammetry methods can be useful in locating physical evidence and determining vehicle speeds and locations. Available 3D modeling software can be used to virtually match photographs or fixed-location video surveillance footage. Dash- or vehicle-mounted camera systems are increasingly being used in light vehicles, commercial vehicles and locomotives. Suppose video footage from a dash camera mounted to one of the vehicles involved in the accident is provided for an accident reconstruction but EDR data is unavailable for either of the vehicles involved. The literature to date describes using still photos to locate fixed objects, using video taken from stationary camera locations to determine the speed of moving objects or using video taken from a moving vehicle to locate fixed objects
Photogrammetry is widely used in the automotive and accident reconstruction communities to extract three-dimensional information from photographs. Prior studies in the literature have demonstrated the accuracy of such methods when photographs contain easily-identifiable, distinct points; however, it is often desirable to determine measurements for locations where a seam, edge, or contour line is available. To exploit such details, an analyst can control the direction that the epipolar line is projected onto the camera plane by strategic selection of photographs. This process constrains the search for the corresponding 3D point to a straight line that can be projected perpendicular to the seam, edge, or contour line. Thus, the goal of this study was to evaluate the modeling accuracy for cases in which an analyst uses epipolar lines in a workflow. To do so, artificial images were created using a computer-generated camera within a computer-assisted drawing environment to allow for a known
Accident reconstructionists will typically document scenes, evidence, vehicles or objects of interest by using 3-dimensional laser scanners. These techniques are well documented, utilized and can be extremely accurate. However, when the subject of documentation involves surfaces that include intricate, highly reflective, and/or complex geometry (motorcycles, wheelchairs, stairs, etc.) the commercially available laser scanners can produce obscuring dense stray and scattered points which results in point clouds that could require tedious manual registration and/or optimization. This paper compares a FARO Focus laser scanner, Pix4DMapper, and Agisoft’s Photoscan point cloud data to FARO ARM measurements of vehicles, other transportation devices and architectural features. It was shown that the Pix4DMapper and Agisoft’s Photoscan point cloud data resulted in detailed and accurate point cloud data compared to the FARO ARM measurements. Additionally, the input data for Pix4DMapper and
Video and photo based photogrammetry software has many applications in the accident reconstruction community including documentation of vehicles and scene evidence. Photogrammetry software has developed in its ease of use, cost, and effectiveness in determining three dimensional data points from two dimensional photographs. Contemporary photogrammetry software packages offer an automated solution capable of generating dense point clouds with millions of 3D data points from multiple images. While alternative modern documentation methods exist, including LiDAR technologies such as 3D scanning, which provide the ability to collect millions of highly accurate points in just a few minutes, the appeal of automated photogrammetry software as a tool for collecting dimensional data is the minimal equipment, equipment costs and ease of use. This paper evaluates the accuracy and capabilities of four automated photogrammetry based software programs to accurately create 3D point clouds, by
This paper presents a methodology for determining the position and speed of objects such as vehicles, pedestrians, or cyclists that are visible in video footage captured with only one camera. Objects are tracked in the video footage based on the change in pixels that represent the object moving. Commercially available programs such as PFTracktm and Adobe After Effectstm contain automated pixel tracking features that record the position of the pixel, over time, two dimensionally using the video’s resolution as a Cartesian coordinate system. The coordinate data of the pixel over time can then be transformed to three dimensional data by ray tracing the pixel coordinates onto three dimensional geometry of the same scene that is visible in the video footage background. This paper explains the automated process of first tracking pixels in the video footage, and then remapping the 2D coordinates onto three dimensional geometry using previously published projection mapping and photogrammetry
Improvements in computer image processing and identification capability have led to programs that can rapidly perform calculations and model the three-dimensional spatial characteristics of objects simply from photographs or video frames. This process, known as structure-from-motion or image based scanning, is a photogrammetric technique that analyzes features of photographs or video frames from multiple angles to create dense surface models or point clouds. Concurrently, unmanned aircraft systems have gained widespread popularity due to their reliability, low-cost, and relative ease of use. These aircraft systems allow for the capture of video or still photographic footage of subjects from unique perspectives. This paper explores the efficacy of using a point cloud created from unmanned aerial vehicle video footage with traditional single-image photogrammetry methods to recreate physical evidence at a crash scene. The unique aspects of photographs or video taken with unmanned aircraft
In the field of accident reconstruction, a reconstructionist will often inspect a crash scene months or years after a crash has occurred. With this passage of time important evidence is sometimes no longer present at the scene (i.e. the vehicles involved in the crash, debris on the roadway, tire marks, gouges, paint marks, etc.). When a scene has not been totally documented with a survey by MAIT or the investigating officers, the reconstructionist may need to rely on police, fire department, security camera, or witness photographs. These photos can be used to locate missing evidence by employing traditional photogrammetric techniques. However, traditional techniques require planar surfaces, matched discrete points, or camera matching at the scene. Sometimes it is not possible to survey discrete points or perform camera matching at the scene due to lack of access (the tops of power poles, elevated bridge features, or objects at a great distance) or for safety reasons (interstate
Total quality is becoming increasingly important for competitiveness. In order to achieve high quality, the requirements must be continuously compared with the results achieved in the process. This is done by means of measurement parameters and comparative values. The acquisition of the data requires appropriate measurement methods. The measurement methods and procedures have to be constantly developed in order to measure more precisely and to generate an even higher quality. Thus, the achieved product quality can be determined absolutely and relatively. If deviations from the planned quality parameters occur, the operator will be able to intervene immediately. The presented procedure is one of the noncontact (optical) measurement methods using CMMs, 3D scanners and 3D cameras. It is a combination of stereo photography and photogrammetry. The measurement system is designed modular from any number of camera-computer-units enabling the serial and parallel interconnection of various
The testing of materials that ablate as a design function requires detailed time history of the ablation process. The rate at which the surface recedes during testing is a critically important measure of the performance of thermal protection system (TPS) materials like heat shields for aerospace vehicles. Photogrammetric recession measurement (PRM) meets these needs by recording the surface of the ablating model during heating in hyperthermal test facilities (arc-jets), using two high-resolution digital cameras capable of recording simultaneously. The cameras are calibrated to yield three-dimensional object space measurement for each stereo pair of images, producing surface recession data over the portion of the model for which both cameras share a view.
Achieving and sustaining high levels of quality is an essential prerequisite for successful and sustainable development. The requirement for the description of the existing quality levels are measurement parameters and comparative values for defining the state of the art. The quality of products is defined by measurements determining information for comparing with the planned parameters. The measurement results are used to determine absolutely and relatively the achieved quality. Products change shape and form within the whole product lifecycle. The more accurate and safe the measurement of form and shape become, the better the product quality can be defined. The methods of 3D-measurement are divided into contact (mechanical) and non-contact (optical) using CMMs, 3D scanners and 3D cameras. Advanced methods exploit e.g. the stereo photography. Therefore, a scanner technology has been developed based on 3D surface stereo photography. Basis for the mathematical processing is the
This paper examines a method for generating a scaled three-dimensional computer model of an accident scene from video footage. This method, which combines the previously published methods of video tracking and camera projection, includes automated mapping of physical evidence through rectification of each frame. Video Tracking is a photogrammetric technique for obtaining three-dimensional data from a scene using video and was described in a 2004 publication titled, “A Video Tracking Photogrammetry Technique to Survey Roadways for Accident Reconstruction” (SAE 2004-01-1221). That paper described a method for generating a three-dimensional computer model of a roadway by using video of a drive-through of an accident scene and processing this video footage through available video tracking software.1,2 The benefit of being able to drive through an accident scene to collect data lies in the speed of such a method, but also in safety, as some accident areas are too heavy with traffic
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