Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.

A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by frame-by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera. Key words: Visua

Using spatio-temporal continuity constraints to enhance visual tracking of moving objects

We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-temporal continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-temporal continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video

Machine vision image quality measurement in cardiac x-ray imaging

The purpose of this work is to report on a machine vision approach for the automated measurement of x-ray image contrast of coronary arteries filled with iodine contrast media during interventional cardiac procedures. A machine vision algorithm was developed that creates a binary mask of the principal vessels of the coronary artery tree by thresholding a standard deviation map of the direction image of the cardiac scene derived using a Frangi filter. Using the mask, average contrast is calculated by tting a Gaussian model to the greyscale profile orthogonal to the vessel centre line at a number of points along the vessel. The algorithm was applied to sections of single image frames from 30 left and 30 right coronary artery image sequences from different patients. Manual measurements of average contrast were also performed on the same images. A Bland-Altman analysis indicates good agreement between the two methods with 95% confidence intervals -0.046 to +0.048 with a mean bias of 0.001. The machine vision algorithm has the potential of providing real-time context sensitive information so that radiographic imaging control parameters could be adjusted on the basis of clinically relevant image content

3D mapping from partial observations: An application to utility mapping

Precise mapping of buried utilities is critical to managing massive urban underground infrastructure and preventing utility incidents. Most current research only focuses on generating such maps based on complete information of underground utilities. However, in real-world practice, it is rare that a full picture of buried utilities can be obtained for such mapping. Therefore, this paper explores the problem of generating maps from partial observations of a scene where the actual world is not fully observed. In particular, we focus on the problem of generating 2D/3D maps of buried utilities using a probabilistic based approach. This has the advantage that the method is generic and can be applied to various sources of utility detections, e.g. manhole observations, sensors, and existing records. In this paper, we illustrate our novel methods based on detections from manhole observations and sensor measurements. This paper makes the following new contributions. It is the first time that partial observations have been used to generate utility maps using optimization based approaches. It is the first time that such a large variety of utilities' properties have been considered, such as location, directions, type and size. Another novel contribution is that different kinds of connections are included to reflect the complex layout and structure of buried utilities. Finally, for the first time to the best of our knowledge, we have integrated utility detection, probability calculation, model formulation and map generation into a single framework. The proposed framework represents all detections using a common language of probability distributions and then formulates the mapping problem as an Integer Linear Programming (ILP) problem and the final map is generated based on the solution with the highest probability sum. The effectiveness of this system is evaluated on synthetic and real data using appropriate evaluation metrics

Learning the Repair Urgency for a Decision Support System for Tunnel Maintenance

The transport network in many countries relies on extended portions which run underground in tunnels. As tunnels age, repairs are required to prevent dangerous collapses. However repairs are expensive and will affect the operational efficiency of the tunnel. We present a decision support system (DSS) based on supervised machine learning methods that learns to predict the risk factor and the resulting repair urgency in the tunnel maintenance planning of a European national rail operator. The data on which the prototype has been built consists of 47 tunnels of varying lengths. For each tunnel, periodic survey inspection data is available for multiple years, as well as other data such as the method of construction of the tunnel. Expert annotations are also available for each 10m tunnel segment for each survey as to the degree of repair urgency which are used for both training and model evaluation. We show that good predictive power can be obtained and discuss the relative merits of a number of learning methods

An Integrated Web-based Decision Support System for Inter-Asset Streetworks Management

Streetworks are the activities conducted in streets such as building or repairing roads, installing or replacing buried utilities, or street furniture. Sustainable streetworks requires an integrated approach taking account of the complex inter-asset relationships between human activities, different city infrastructure assets and the environment. To facilitate decision making by relevant stakeholders, an integrated web-based decision support system is presented in this paper which combines experts' domain knowledge, multiple geospatial datasets and an inference engine for automated inference of potential consequences. Users' feedback collected from two workshops showed that the system is widely considered as a potentially useful tool for practitioners

A decision support system to proactively manage subsurface utilities

Critical infrastructure assets are defined in terms of their purpose (e.g. roads, water, and energy) yet the ground, which supports these assets, can also be considered a critical asset leading to the conclusion that any assessment of critical infrastructure must consider the ground in that assessment. While the interdependency of critical infrastructures is recognised, the consequences of failing to recognise the ground as an asset can lead to failure of the infrastructure it supports. This motivates the need for a decision support system for subsurface utilities that takes into account the surrounding ground and the overlying road structure. These facilities mostly exist in an urban environment. The ground supports the road and the underlying utility which means the failure of any of these assets (road, ground, or utility) can trigger a failure in the others, the most extreme example being the collapse of roads due to erosion of the supporting ground by a leaking pipe. This paper describes the principles that underpin a novel decision support system for those engaged in street works of any kind, and how a multidisciplinary approach is being used to create a practical toolkit to reduce risk and minimise disruption to proactively manage subsurface utilities using site observations and investigations, public and private databases, expert opinions captured in a number of ontologies and an inference engine to produce guidance that takes into account risk and sustainability criteria

ATU-DSS: Knowledge-Driven Data Integration and Reasoning for Sustainable Subsurface Inter-Asset Management

Urban infrastructure assets perform critical functions to the health and well-being of the society. In this paper, we present a prototype decision support system for sustainable subsurface inter-asset management. To the best of the authors’ knowledge, this work is the first on assessing the underground space by considering the inter-asset dependencies using semantic technologies. Based on a family of interlinked city infrastructure asset ontologies describing the ground, roads and buried utilities (e.g. water pipes), various datasets are integrated and logical rules are developed to describe the intra-asset and inter-asset relationships. An inference engine is employed to exploit the knowledge and data for assessing the potential impact of an event. This system can be beneficial to a wide range of stakeholders (e.g. utility incident managers) for quickly gathering of the localised contextual data and identifying potential consequences from what may appear as an insignificant trigger. A video demonstrating the prototype is available at: http://bit.ly/2mdyIY4

Can adverse maternal and perinatal outcomes be predicted when blood pressure becomes elevated? Secondary analyses from the CHIPS (Control of Hypertension In Pregnancy Study) randomized controlled trial.

INTRODUCTION: For women with chronic or gestational hypertension in CHIPS (Control of Hypertension In Pregnancy Study, NCT01192412), we aimed to examine whether clinical predictors collected at randomization could predict adverse outcomes. MATERIAL AND METHODS: This was a planned, secondary analysis of data from the 987 women in the CHIPS Trial. Logistic regression was used to examine the impact of 19 candidate predictors on the probability of adverse perinatal (pregnancy loss or high level neonatal care for >48 h, or birthweight <10th percentile) or maternal outcomes (severe hypertension, preeclampsia, or delivery at <34 or <37 weeks). A model containing all candidate predictors was used to start the stepwise regression process based on goodness of fit as measured by the Akaike information criterion. For face validity, these variables were forced into the model: treatment group ("less tight" or "tight" control), antihypertensive type at randomization, and blood pressure within 1 week before randomization. Continuous variables were represented continuously or dichotomized based on the smaller p-value in univariate analyses. An area-under-the-receiver-operating-curve (AUC ROC) of ≥0.70 was taken to reflect a potentially useful model. RESULTS: Point estimates for AUC ROC were <0.70 for all but severe hypertension (0.70, 95% CI 0.67-0.74) and delivery at <34 weeks (0.71, 95% CI 0.66-0.75). Therefore, no model warranted further assessment of performance. CONCLUSIONS: CHIPS data suggest that when women with chronic hypertension develop an elevated blood pressure in pregnancy, or formerly normotensive women develop new gestational hypertension, maternal and current pregnancy clinical characteristics cannot predict adverse outcomes in the index pregnancy