Initializing Vision Based Trackers Using Semantic Texton Forests

Vision based tracking can provide the spatial location of project related entities such as equipment, workers, and materials in a large-scale congested construction site. It tracks entities in a video stream by inferring their motion. To initiate the process, it is required to determine the pixel areas of the entities to be tracked in the following consecutive video frames. For the purpose of fully automating the process, this paper presents an automated way of initializing trackers using Semantic Texton Forests (STFs) method. STFs method performs simultaneously the segmentation of the image and the classification of the segments based on the low-level semantic information and the context information. In this paper, STFs method is tested in the case of wheel loaders recognition. In the experiments, wheel loaders are further divided into several parts such as wheels and body parts to help learn the context information. The results show 79% accuracy of recognizing the pixel areas of the wheel loader. These results signify that STFs method has the potential to automate the initialization process of vision based tracking.

Machine Vision-Based Infrastructure As-Built Documentation Using Edge Points

The existing machine vision-based 3D reconstruction software programs provide a promising low-cost and in some cases automatic solution for infrastructure as-built documentation. However in several steps of the reconstruction process, they only rely on detecting and matching corner-like features in multiple views of a scene. Therefore, in infrastructure scenes which include uniform materials and poorly textured surfaces, these programs fail with high probabilities due to lack of feature points. Moreover, except few programs that generate dense 3D models through significantly time-consuming algorithms, most of them only provide a sparse reconstruction which does not necessarily include required points such as corners or edges; hence these points have to be manually matched across different views that could make the process considerably laborious. To address these limitations, this paper presents a video-based as-built documentation method that automatically builds detailed 3D maps of a scene by aligning edge points between video frames. Compared to corner-like features, edge points are far more plentiful even in untextured scenes and often carry important semantic associations. The method has been tested for poorly textured infrastructure scenes and the results indicate that a combination of edge and corner-like features would allow dealing with a broader range of scenes.

Micro-measurement and monitoring system for ageing underground infrastructure (Underground M3)

Advances in the development of computer vision, miniature Micro-Electro-Mechanical Systems (MEMS) and Wireless Sensor Network (WSN) offer intriguing possibilities that can radically alter the paradigms underlying existing methods of condition assessment and monitoring of ageing civil engineering infrastructure. This paper describes some of the outcomes of the European Science Foundation project "Micro-Measurement and Monitoring System for Ageing Underground Infrastructures (Underground M3)". The main aim of the project was to develop a system that uses a tiered approach to monitor the degree and rate of tunnel deterioration. The system comprises of (1) Tier 1: Micro-detection using advances in computer vision and (2) Tier 2: Micro-monitoring and communication using advances in MEMS and WSN. These potentially low-cost technologies will be able to reduce costs associated with end-of-life structures, which is essential to the viability of rehabilitation, repair and reuse. The paper describes the actual deployment and testing of these innovative monitoring tools in tunnels of London Underground, Prague Metro and Barcelona Metro. © 2012 Taylor & Francis Group.

Achievements and challenges in machine vision-based inspection of large concrete structures

Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated.