Real-time Spatial Detection and Tracking of Resources in a Construction Environment

Construction accidents with heavy equipment and bad decision making can be based on poor knowledge of the site environment and in both cases may lead to work interruptions and costly delays. Supporting the construction environment with real-time generated three-dimensional (3D) models can help preventing accidents as well as support management by modeling infrastructure assets in 3D. Such models can be integrated in the path planning of construction equipment operations for obstacle avoidance or in a 4D model that simulates construction processes. Detecting and guiding resources, such as personnel, machines and materials in and to the right place on time requires methods and technologies supplying information in real-time. This paper presents research in real-time 3D laser scanning and modeling using high range frame update rate scanning technology. Existing and emerging sensors and techniques in three-dimensional modeling are explained. The presented research successfully developed computational models and algorithms for the real-time detection, tracking, and three-dimensional modeling of static and dynamic construction resources, such as workforce, machines, equipment, and materials based on a 3D video range camera. In particular, the proposed algorithm for rapidly modeling three-dimensional scenes is explained. Laboratory and outdoor field experiments that were conducted to validate the algorithm’s performance and results are discussed

Security in Beijing 2008 and Beyond

In the lead-up to the 2008 Olympics, HRIC has been monitoring key aspects of preparationsfor the Beijing Games, including sponsorships, venue construction and the Chinese government's commitments as host of the Games. This update focuses on security preparations for the Olympics, with an emphasis on the use of advanced technology to secure Beijing and the Games. It also examines human rights concerns raised by the installation of this sophisticated security apparatus. Finally, this update explores the role of international corporations in security preparations, and presents available informationabout contracts that have already been reached

Benefits and barriers of construction project monitoring using hi-resolution automated cameras

A more rapid and widespread use and implementation of technology in construction often fails since its benefits and limitations remain somewhat unclear. Project control is one of the most variable and time consuming task of construction project managers and superintendents, and yet continues to be mostly a manual task. Controlling tasks such as tracking and updating project schedules can be assisted through remotely operating technology such as hi-resolution cameras that can provide construction management and other users with imaging feeds of job site activities. Although construction cameras have been around for many years the costs, benefits, and barriers of their use have not been investigated nor quantified in detail. Subsequently, definitions and understanding vary widely, making it difficult for decision makers at the organizational level to decide on the investment in camera technology. This thesis reviews the status of hi-resolution cameras and their present use in construction. Results of a multi-phased survey to industry professionals were collected in order to identify benefits and barriers and develop a cost-benefit model that can be used for implementation technology in construction.M.S.Committee Chair: Jochen Teizer; Committee Member: Ioannis Brilakis; Committee Member: Michael Meye

Transportation, Terrorism and Crime: Deterrence, Disruption and Resilience

Abstract: Terrorists likely have adopted vehicle ramming as a tactic because it can be carried out by an individual (or “lone wolf terrorist”), and because the skills required are minimal (e.g. the ability to drive a car and determine locations for creating maximum carnage). Studies of terrorist activities against transportation assets have been conducted to help law enforcement agencies prepare their communities, create mitigation measures, conduct effective surveillance and respond quickly to attacks. This study reviews current research on terrorist tactics against transportation assets, with an emphasis on vehicle ramming attacks. It evaluates some of the current attack strategies, and the possible mitigation or response tactics that may be effective in deterring attacks or saving lives in the event of an attack. It includes case studies that can be used as educational tools for understanding terrorist methodologies, as well as ordinary emergencies that might become a terrorist’s blueprint

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video

TRUSS: Tracking Risk with Ubiquitous Smart Sensing

We present TRUSS, or Tracking Risk with Ubiquitous Smart Sensing, a novel system that infers and renders safety context on construction sites by fusing data from wearable devices, distributed sensing infrastructure, and video. Wearables stream real-time levels of dangerous gases, dust, noise, light quality, altitude, and motion to base stations that synchronize the mobile devices, monitor the environment, and capture video. At the same time, low-power video collection and processing nodes track the workers as they move through the view of the cameras, identifying the tracks using information from the sensors. These processes together connect the context-mining wearable sensors to the video; information derived from the sensor data is used to highlight salient elements in the video stream. The augmented stream in turn provides users with better understanding of real-time risks, and supports informed decision-making. We tested our system in an initial deployment on an active construction site.Intel CorporationMassachusetts Institute of Technology. Media LaboratoryEni S.p.A. (Firm

Space programs summary no. 37-29, volume VI FOR the period July 1, 1964 to August 31, 1964. Space exploration programs and space sciences

Summaries covering system development and operations of Ranger VII, Surveyor, and Mariner spacecraft, and engineering development at deep space network facilitie

Automatic Scaffolding Productivity Measurement through Deep Learning

This study developed a method to automatically measure scaffolding productivity by extracting and analysing semantic information from onsite vision data