Current Projects
PROGRESSIVE SITE MODELING WITH VIDEOGRAMMETRY
NSF Grant #0800170, PI: Ioannis Brilakis, $231,407
The objective of this research is to test the hypothesis that a mobile, calibrated set of high resolution video cameras can be used to acquire the spatial data of a construction site with the assistance of a novel videogrammetric method. Under this method, video streams are initially collected from the camera set that is progressively traversed around a construction site. The possible correspondences of each point in each video camera’s view are computed (epipolar lines) and the corresponding points are matched using a novel window similarity matching method that compares the video frame along each epipolar line. Based on each match and the camera calibration, the depth value of each point is computed, and the depth map (point cloud) of the scene is generated. In each subsequent frame, all points with a previously identified correspondence in the other video camera’s frame are tracked using established 2D point tracking techniques. The resulting point cloud at each frame is then converted to a 3D surface using intelligent proximity algorithms, and the visual data are overlaid to produce a photorealistic, rendered 3D surface. Leading student researchers: Habib Fathi and Abbas Rashidi
GRS: PROGRESSIVE SITE MODELING WITH VIDEOGRAMMETRY
NSF Grant #0943112, PI: Ioannis Brilakis, $36,978
This project is a supplement to the project above and aims to engage an underrepresented graduate student, Ms. Stephanie German, in the core research of the parent grant. Her scope of work will include the activities needed to validate the point pair tracking and relative geo-referencing methods proposed originally. The supplement was requested due to an additional predecessor research activity (automate camera system calibration) that was discovered to be necessary after the first 6 months of tests, and was not taken into account in the original budget. Leading student researcher: Stephanie German
AUTOMATED VISION TRACKING OF PROJECT RELATED ENTITIES
NSF Grant #0625643, PI: Ioannis Brilakis, $299,739
This research aims to design an automated vision tracking method that reports the 4D location (spatial coordinates and time) of distinctly shaped, project related entities, such as construction equipment, personnel, and materials of standard sizes and shapes. Under this method, two or more self-calibrated, outdoor wireless video cameras are initially placed at a project site and collect video-streams. Using construction materials and shapes visual recognition techniques, each project related entity on the cameras’ field of view is identified as an "interesting" pattern to track. Established tracking tools are then used in each subsequent frame of the video stream to track the movement of the identified "interesting" entity while it operates within the cameras' viewing spectrum. Results from this work can be found here. Leading student researchers: Man Woo Park and Atefe Makhmalbaf
IREE: AUTOMATED VISION TRACKING OF PROJECT RELATED ENTITIES
NSF Grant #0738417, PI: Ioannis Brilakis, $37,250
This international collaboration sent 3 US students to the Aristotle University of Thessaloniki (AUTh), Greece, for 4 months. The students tested the tracking method invented and prototyped in the project above on several types of sites of the Egnatia Odos motorway project, such as cantilevered bridge construction, tunnel face excavation, and interchange construction. These sites were under heavy equipment, personnel and materials traffic. Egnatia Odos is an $8 billion, 670km project that aims to create a central East-West artery to connect Turkey in the east with the Ionian Sea port of Igoumenitsa in the west. The foreign collaborator of this project, Pr. Demos Angelides, is the Chairman of the Civil and Environmental Engineering Department at AUTh and acted as the host and local advisor for our students. Results from the work can be found here. Leading student researcher: Gauri Jog
QUANTITATIVE SURFACE DEFECTS ASSESSMENT FOR CONCRETE INSPECTION
The objective of this research is to design an automated, quantitative model for locating and identifying defects, such as air pockets and discoloration, on concrete surfaces. Under this model, image processing techniques are used to locate the defects and determine their number, size and degree of impact. This information is then used to quantify the concrete surface quality and provide a recommendation for setting specification standards for cast in place and architectural concrete. Results from this work can be found here. Leading student researcher: Zhenhua Zhu
REAL TIME CONCRETE DAMAGE VISUAL ASSESSMENT FOR FIRST RESPONDERS
The objective of this research is to test the following hypothesis: The risk of structural collapse after an earthquake in reinforced concrete frame structures due to column failure can be reasonably estimated by automatically recognizing these columns and the type of damage inflicted on them through a video camera mounted in a first responder’s hardhat. If this hypothesis is validated, it will assist in automating the prediction of potential structural collapse of concrete buildings for first responders (firemen, policemen and medics) who must enter these damaged buildings to perform essential emergency response functions after earthquakes. Preliminary results from this work can be found here. Leading student researcher: Zhenhua Zhu
PATTERN BASED RECOGNITION OF CONSTRUCTION OBJECTS FROM VISUAL DATA
This is a project focused on fundamental research the will enable automated, model based recognition of construction objects. It entails the creation of visual pattern recognition models for a variety of construction object types. The purpose is to assist as-built modelers of facilities by automatically recognizing the common and more frequent objects automatically, leaving only the specialty items to the hands of the modeler. The validation of this project will be based on the software platform of INOVX Inc., who is the industrial collaborator to this project. Leading student researchers: Aaron Henry and Aswathy Sivaram