Comparative study of vision tracking methods for tracking of construction site resources

Vision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources.

Automated sparse 3D point cloud generation of infrastructure using its distinctive visual features

The commercial far-range (>10 m) spatial data collection methods for acquiring infrastructure’s geometric data are not completely automated because of the necessary manual pre- and/or post-processing work. The required amount of human intervention and, in some cases, the high equipment costs associated with these methods impede their adoption by the majority of infrastructure mapping activities. This paper presents an automated stereo vision-based method, as an alternative and inexpensive solution, to producing a sparse Euclidean 3D point cloud of an infrastructure scene utilizing two video streams captured by a set of two calibrated cameras. In this process SURF features are automatically detected and matched between each pair of stereo video frames. 3D coordinates of the matched feature points are then calculated via triangulation. The detected SURF features in two successive video frames are automatically matched and the RANSAC algorithm is used to discard mismatches. The quaternion motion estimation method is then used along with bundle adjustment optimization to register successive point clouds. The method was tested on a database of infrastructure stereo video streams. The validity and statistical significance of the results were evaluated by comparing the spatial distance of randomly selected feature points with their corresponding tape measurements.

Automated vision tracking of project related entities

Tracking of project related entities such as construction equipment, materials, and personnel is used to calculate productivity, detect travel path conflicts, enhance the safety on the site, and monitor the project. Radio frequency tracking technologies (Wi-Fi, RFID, UWB) and GPS are commonly used for this purpose. However, on large-scale sites, deploying, maintaining and removing such systems can be costly and time-consuming. In addition, privacy issues with personnel tracking often limits the usability of these technologies on construction sites. This paper presents a vision based tracking framework that holds promise to address these limitations. The framework uses videos from a set of two or more static cameras placed on construction sites. In each camera view, the framework identifies and tracks construction entities providing 2D image coordinates across frames. Combining the 2D coordinates based on the installed camera system (the distance between the cameras and the view angles of them), 3D coordinates are calculated at each frame. The results of each step are presented to illustrate the feasibility of the framework.

Auto-calibration of a camera system using Image Alignment

Calibration of a camera system is a necessary step in any stereo metric process. It correlates all cameras to a common coordinate system by measuring the intrinsic and extrinsic parameters of each camera. Currently, manual calibration of a camera system is the only way to achieve calibration in civil engineering operations that require stereo metric processes (photogrammetry, videogrammetry, vision based asset tracking, etc). This type of calibration however is time-consuming and labor-intensive. Furthermore, in civil engineering operations, camera systems are exposed to open, busy sites. In these conditions, the position of presumably stationary cameras can easily be changed due to external factors such as wind, vibrations or due to an unintentional push/touch from personnel on site. In such cases manual calibration must be repeated. In order to address this issue, several self-calibration algorithms have been proposed. These algorithms use Projective Geometry, Absolute Conic and Kruppa Equations and variations of these to produce processes that achieve calibration. However, most of these methods do not consider all constraints of a camera system such as camera intrinsic constraints, scene constraints, camera motion or varying camera intrinsic properties. This paper presents a novel method that takes all constraints into consideration to auto-calibrate cameras using an image alignment algorithm originally meant for vision based tracking. In this method, image frames are taken from cameras. These frames are used to calculate the fundamental matrix that gives epipolar constraints. Intrinsic and extrinsic properties of cameras are acquired from this calculation. Test results are presented in this paper with recommendations for further improvement.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

Automated On-Site Retrieval of Project Information

Among several others, the on-site inspection process is mainly concerned with finding the right design and specifications information needed to inspect each newly constructed segment or element. While inspecting steel erection, for example, inspectors need to locate the right drawings for each member and the corresponding specifications sections that describe the allowable deviations in placement among others. These information seeking tasks are highly monotonous, time consuming and often erroneous, due to the high similarity of drawings and constructed elements and the abundance of information involved which can confuse the inspector. To address this problem, this paper presents the first steps of research that is investigating the requirements of an automated computer vision-based approach to automatically identify “as-built” information and use it to retrieve “as-designed” project information for field construction, inspection, and maintenance tasks. Under this approach, a visual pattern recognition model was developed that aims to allow automatic identification of construction entities and materials visible in the camera’s field of view at a given time and location, and automatic retrieval of relevant design and specifications information.

Automated 3D Vision Tracking for Project Control Support

On-site tracking in open construction sites is often difficult because of the large amounts of items that are present and need to be tracked. Additionally, the amounts of occlusions/obstructions present create a highly complex tracking environment. Existing tracking methods are based mainly on Radio Frequency technologies, including Global Positioning Systems (GPS), Radio Frequency Identification (RFID), Bluetooth and Wireless Fidelity (Wi-Fi, Ultra-Wideband, etc). These methods require considerable amounts of pre-processing time since they need to manually deploy tags and keep record of the items they are placed on. In construction sites with numerous entities, tags installation, maintenance and decommissioning become an issue since it increases the cost and time needed to implement these tracking methods. This paper presents a novel method for open site tracking with construction cameras based on machine vision. According to this method, video feed is collected from on site video cameras, and the user selects the entity he wishes to track. The entity is tracked in each video using 2D vision tracking. Epipolar geometry is then used to calculate the depth of the marked area to provide the 3D location of the entity. This method addresses the limitations of radio frequency methods by being unobtrusive and using inexpensive, and easy to deploy equipment. The method has been implemented in a C++ prototype and preliminary results indicate its effectiveness

Validation of Vision Tracking at Egnatia Odos Motorway

Tracking applications provide real time on-site information that can be used to detect travel path conflicts, calculate crew productivity and eliminate unnecessary processes at the site. This paper presents the validation of a novel vision based tracking methodology at the Egnatia Odos Motorway in Thessaloniki, Greece. Egnatia Odos is a motorway that connects Turkey with Italy through Greece. Its multiple open construction sites serves as an ideal multi-site test bed for validating construction site tracking methods. The vision based tracking methodology uses video cameras and computer algorithms to calculate the 3D position of project related entities (e.g. personnel, materials and equipment) in construction sites. The approach provides an unobtrusive, inexpensive way of effectively identifying and tracking the 3D location of entities. The process followed in this study starts by acquiring video data from multiple synchronous cameras at several large scale project sites of Egnatia Odos, such as tunnels, interchanges and bridges under construction. Subsequent steps include the evaluation of the collected data and finally, performing the 3D tracking operations on selected entities (heavy equipment and personnel). The accuracy and precision of the method's results is evaluated by comparing it with the actual 3D position of the object, thus assessing the 3D tracking method's effectiveness.

Innovative Stereo Vision-Based Approach to Generate Dense Depth Map of Transportation Infrastructure

Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.

Automated 3D Structure Inference of Civil Infrastructure Using a Stereo Camera Set

The commercial far-range (>10m) infrastructure spatial data collection methods are not completely automated. They need significant amount of manual post-processing work and in some cases, the equipment costs are significant. This paper presents a method that is the first step of a stereo videogrammetric framework and holds the promise to address these issues. Under this method, video streams are initially collected from a calibrated set of two video cameras. For each pair of simultaneous video frames, visual feature points are detected and their spatial coordinates are then computed. The result, in the form of a sparse 3D point cloud, is the basis for the next steps in the framework (i.e., camera motion estimation and dense 3D reconstruction). A set of data, collected from an ongoing infrastructure project, is used to show the merits of the method. Comparison with existing tools is also shown, to indicate the performance differences of the proposed method in the level of automation and the accuracy of results.

Recovering 3D Structure of Poorly Textured Infrastructure Scenes Using Point and Line Features

Most of the existing automated machine vision-based techniques for as-built documentation of civil infrastructure utilize only point features to recover the 3D structure of a scene. However it is often the case in man-made structures that not enough point features can be reliably detected (e.g. buildings and roofs); this can potentially lead to the failure of these techniques. To address the problem, this paper utilizes the prominence of straight lines in infrastructure scenes. It presents a hybrid approach that benefits from both point and line features. A calibrated stereo set of video cameras is used to collect data. Point and line features are then detected and matched across video frames. Finally, the 3D structure of the scene is recovered by finding 3D coordinates of the matched features. The proposed approach has been tested on realistic outdoor environments and preliminary results indicate its capability to deal with a variety of scenes.

IREE: Automated Vision Tracking of Project Related Entities

The purpose of this supplemental project was to collect invaluable data from the large-scale construction sites of Egnatia Odos motorway needed to validate a novel automated vision-tracking method created under the parent grant. For this purpose, one US graduate and three US undergraduate students traveled to Greece for 4 months and worked together with 2 Greek graduate students of the local faculty collaborator. This team of students monitored project activities and scheduled data collection trips on a daily basis, setup a mobile video data collection lab on the back of a truck, and drove to various sites every day to collect hundreds of hours of video from multiple cameras on a large variety of activities ranging from soil excavation to bridge construction. The US students were underrepresented students from minority groups who had never visited a foreign country. As a result, this trip was a major life experience to them. They learned how to live in a non-English speaking country, communicate with Greek students, workers and engineers. They lead a project in a very unfamiliar environment, troubleshoot myriad problems that hampered their progress daily and, above all, how to collaborate effectively and efficiently with other cultures. They returned to the US more mature, with improved leadership and problem-solving skills and a wider perspective of their profession.

Three-Dimensional Tracking of Construction Resources using an On-Site Camera System

Vision trackers have been proposed as a promising alternative for tracking at large-scale, congested construction sites. They provide the location of a large number of entities in a camera view across frames. However, vision trackers provide only two-dimensional (2D) pixel coordinates, which are not adequate for construction applications. This paper proposes and validates a method that overcomes this limitation by employing stereo cameras and converting 2D pixel coordinates to three-dimensional (3D) metric coordinates. The proposed method consists of four steps: camera calibration, camera pose estimation, 2D tracking, and triangulation. Given that the method employs fixed, calibrated stereo cameras with a long baseline, appropriate algorithms are selected for each step. Once the first two steps reveal camera system parameters, the third step determines 2D pixel coordinates of entities in subsequent frames. The 2D coordinates are triangulated on the basis of the camera system parameters to obtain 3D coordinates. The methodology presented in this paper has been implemented and tested with data collected from a construction site. The results demonstrate the suitability of this method for on-site tracking purposes.

Innovative Stereo Vision-Based Approach to Generate Dense Depth Map of Transportation Infrastructure

Three-dimensional (3-D) spatial data of a transportation infrastructure contain useful information for civil engineering applications, including as-built documentation, on-site safety enhancements, and progress monitoring. Several techniques have been developed for acquiring 3-D point coordinates of infrastructure, such as laser scanning. Although the method yields accurate results, the high device costs and human effort required render the process infeasible for generic applications in the construction industry. A quick and reliable approach, which is based on the principles of stereo vision, is proposed for generating a depth map of an infrastructure. Initially, two images are captured by two similar stereo cameras at the scene of the infrastructure. A Harris feature detector is used to extract feature points from the first view, and an innovative adaptive window-matching technique is used to compute feature point correspondences in the second view. A robust algorithm computes the nonfeature point correspondences. Thus, the correspondences of all the points in the scene are obtained. After all correspondences have been obtained, the geometric principles of stereo vision are used to generate a dense depth map of the scene. The proposed algorithm has been tested on several data sets, and results illustrate its potential for stereo correspondence and depth map generation.