Exploiting low-cost 3D imagery for the purposes of detecting and analyzing pavement distresses

Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS

New instruments and technologies for Cultural Heritage survey: full integration between point clouds and digital photogrammetry

In the last years the Geomatic Research Group of the Politecnico di Torino faced some new research topics about new instruments for point cloud generation (e.g. Time of Flight cameras) and strong integration between multi-image matching techniques and 3D Point Cloud information in order to solve the ambiguities of the already known matching algorithms. ToF cameras can be a good low cost alternative to LiDAR instruments for the generation of precise and accurate point clouds: up to now the application range is still limited but in a near future they will be able to satisfy the most part of the Cultural Heritage metric survey requirements. On the other hand multi-image matching techniques with a correct and deep integration of the point cloud information can give the correct solution for an "intelligent" survey of the geometric object break-lines, which are the correct starting point for a complete survey. These two research topics are strictly connected to a modern Cultural Heritage 3D survey approach. In this paper after a short analysis of the achieved results, an alternative possible scenario for the development of the metric survey approach inside the wider topic of Cultural Heritage Documentation is reporte

A model-based approach to recovering the structure of a plant from images

We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention

Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

Implementation of explosion safety regulations in design of a mobile robot for coal mines

The article focuses on specific challenges of the design of a reconnaissance mobile robotic system aimed for inspection in underground coal mine areas after a catastrophic event. Systems that are designated for these conditions must meet specific standards and regulations. In this paper is discussed primarily the main conception of meeting explosion safety regulations of European Union 2014/34/EU (also called ATEX-from French "Appareils destines a etre utilises en ATmospheres Explosives") for Group I (equipment intended for use in underground mines) and Category M1 (equipment designed for operation in the presence of an explosive atmosphere). An example of a practical solution is described on main subsystems of the mobile robot TeleRescuera teleoperated robot with autonomy functions, a sensory subsystem with multiple cameras, three-dimensional (3D) mapping and sensors for measurement of gas concentration, airflow, relative humidity, and temperatures. Explosion safety is ensured according to the Technical Report CLC/TR 60079-33 "s" by two main independent protections-mechanical protection (flameproof enclosure) and electrical protection (automatic methane detector that disconnects power when methane breaches the enclosure and gets inside the robot body).Web of Science811art. no. 230

A best view selection in meetings through attention analysis using a multi-camera network

Human activity analysis is an essential task in ambient intelligence and computer vision. The main focus lies in the automatic analysis of ongoing activities from a multi-camera network. One possible application is meeting analysis which explores the dynamics in meetings using low-level data and inferring high-level activities. However, the detection of such activities is still very challenging due to the often corrupted or imprecise low-level data. In this paper, we present an approach to understand the dynamics in meetings using a multi-camera network, consisting of fixed ambient and portable close-up cameras. As a particular application we are aiming to find the most informative video stream, for example as a representative view for a remote participant. Our contribution is threefold: at first, we estimate the extrinsic parameters of the portable close-up cameras based on head positions. Secondly, we find common overlapping areas based on the consensus of people’s orientation. And thirdly, the most informative view for a remote participant is estimated using common overlapping areas. We evaluated our proposed approach and compared it to a motion estimation method. Experimental results show that we can reach an accuracy of 74% compared to manually selected views

A Feasibility Study on the Use of a Structured Light Depth-Camera for Three-Dimensional Body Measurements of Dairy Cows in Free-Stall Barns

Frequent checks on livestock\u2019s body growth can help reducing problems related to cow infertility or other welfare implications, and recognizing health\u2019s anomalies. In the last ten years, optical methods have been proposed to extract information on various parameters while avoiding direct contact with animals\u2019 body, generally causes stress. This research aims to evaluate a new monitoring system, which is suitable to frequently check calves and cow\u2019s growth through a three-dimensional analysis of their bodies\u2019 portions. The innovative system is based on multiple acquisitions from a low cost Structured Light Depth-Camera (Microsoft Kinect\u2122 v1). The metrological performance of the instrument is proved through an uncertainty analysis and a proper calibration procedure. The paper reports application of the depth camera for extraction of different body parameters. Expanded uncertainty ranging between 3 and 15 mm is reported in the case of ten repeated measurements. Coef\ufb01cients of determination R2> 0.84 and deviations lower than 6% from manual measurements where in general detected in the case of head size, hips distance, withers to tail length, chest girth, hips, and withers height. Conversely, lower performances where recognized in the case of animal depth (R2 = 0.74) and back slope (R2 = 0.12)