A fiber Bragg grating sensor system for train axle counting

Railway signaling facilitates two main functions, namely, train detection and train control, in order to maintain safe separations among the trains. Track circuits are the most commonly used train detection means with the simple open/close circuit principles; and subsequent adoption of axle counters further allows the detection of trains under adverse track conditions. However, with electrification and power electronics traction drive systems, aggravated by the electromagnetic interference in the vicinity of the signaling system, railway engineers often find unstable or even faulty operations of track circuits and axle counting systems, which inevitably jeopardizes the safe operation of trains. A new means of train detection, which is completely free from electromagnetic interference, is therefore required for the modern railway signaling system. This paper presents a novel optical fiber sensor signaling system. The sensor operation, field setup, axle detection solution set, and test results of an installation in a trial system on a busy suburban railway line are given.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.

Scene invariant crowd counting for real-time surveillance

Automated crowd counting allows excessive crowding to be detected immediately, without the need for constant human surveillance. Current crowd counting systems are location specific, and for these systems to function properly they must be trained on a large amount of data specific to the target location. As such, configuring multiple systems to use is a tedious and time consuming exercise. We propose a scene invariant crowd counting system which can easily be deployed at a different location to where it was trained. This is achieved using a global scaling factor to relate crowd sizes from one scene to another. We demonstrate that a crowd counting system trained at one viewpoint can achieve a correct classification rate of 90% at a different viewpoint.

Mapping equity and diversity : from counting to culture change

This paper investigates the links between various approaches to managing equity and diversity and their effectiveness in changing the measures of inclusivity of women in organisations as a means of auditing and mapping managing diversity outcomes in Australia. The authors argue that managing diversity is more than changing systems and counting numbers it is also about managing the substantive culture change required in order to achieve inclusivity particularly intercultural inclusivity. Research in one sector of the education industry that investigated the competency skills required for culture change is offered as a model or guide for understanding and reflecting upon intercultural competency and its sequential development.

Crowd counting using group tracking and local features

In public venues, crowd size is a key indicator of crowd safety and stability. In this paper we propose a crowd counting algorithm that uses tracking and local features to count the number of people in each group as represented by a foreground blob segment, so that the total crowd estimate is the sum of the group sizes. Tracking is employed to improve the robustness of the estimate, by analysing the history of each group, including splitting and merging events. A simplified ground truth annotation strategy results in an approach with minimal setup requirements that is highly accurate.

Hospital beds : A primer for counting and comparing

There is considerable public, political and professional debate about the need for additional hospital beds in Australia. However, there is no clarity in regard to the definition, meaning and significance of hospital bed counts. Relative to population, there has been a total decline in bed availability in Australia over the past 15 years of 14.6% (22.9% for public hospital beds). This decline is partly offset by reductions in length of stay and changes to models of care; however, the net effect is increased bed occupancy which has in turn resulted in system-wide congestion. Future bed capability needs to be better planned to meet growing demands while at the same time continuing trends for more efficient use. Future planning should be based in part on weighted bed capability matched to need.

Property damage crash equivalency factors to solve crash frequency-severity dilemma: Case study on South Korean rural roads

Safety interventions (e.g., median barriers, photo enforcement) and road features (e.g., median type and width) can influence crash severity, crash frequency, or both. Both dimensions—crash frequency and crash severity—are needed to obtain a full accounting of road safety. Extensive literature and common sense both dictate that crashes are not created equal, with fatalities costing society more than 1,000 times the cost of property damage crashes on average. Despite this glaring disparity, the profession has not unanimously embraced or successfully defended a nonarbitrary severity weighting approach for analyzing safety data and conducting safety analyses. It is argued here that the two dimensions (frequency and severity) are made available by intelligently and reliably weighting crash frequencies and converting all crashes to property-damage-only crash equivalents (PDOEs) by using comprehensive societal unit crash costs. This approach is analogous to calculating axle load equivalents in the prediction of pavement damage: for instance, a 40,000-lb truck causes 4,025 times more stress than does a 4,000-lb car and so simply counting axles is not sufficient. Calculating PDOEs using unit crash costs is the most defensible and nonarbitrary weighting scheme, allows for the simple incorporation of severity and frequency, and leads to crash models that are sensitive to factors that affect crash severity. Moreover, using PDOEs diminishes the errors introduced by underreporting of less severe crashes—an added benefit of the PDOE analysis approach. The method is illustrated with rural road segment data from South Korea (which in practice would develop PDOEs with Korean crash cost data).

Scene invariant crowd counting

This paper describes a scene invariant crowd counting algorithm that uses local features to monitor crowd size. Unlike previous algorithms that require each camera to be trained separately, the proposed method uses camera calibration to scale between viewpoints, allowing a system to be trained and tested on different scenes. A pre-trained system could therefore be used as a turn-key solution for crowd counting across a wide range of environments. The use of local features allows the proposed algorithm to calculate local occupancy statistics, and Gaussian process regression is used to scale to conditions which are unseen in the training data, also providing confidence intervals for the crowd size estimate. A new crowd counting database is introduced to the computer vision community to enable a wider evaluation over multiple scenes, and the proposed algorithm is tested on seven datasets to demonstrate scene invariance and high accuracy. To the authors' knowledge this is the first system of its kind due to its ability to scale between different scenes and viewpoints.

A visual description of early-number counting, addition and subtraction knowledge

Early-number is a rich fabric of interconnected ideas that is often misunderstood and thus taught in ways that do not lead to rich understanding. In this presentation, a visual language is used to describe the organisation of this domain of knowledge. This visual language is based upon Piaget’s notion of reflective abstraction (Dubinsky, 1991; Piaget, 1977/2001), and thus captures the epistemological associations that link the problems, concepts and representations of the domain. The constructs of this visual language are introduced and then applied to the early-number domain. The introduction to this visual language may prompt reflection upon its suitability and significance to the description of other domains of knowledge. Through such a process of analysis and description, the visual language may serve as a scaffold for enhancing pedagogical content knowledge and thus ultimately improve learning outcomes.

Scene invariant crowd counting and crowd occupancy analysis

In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities. A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult. In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data. Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.

Investing in the future : analysis of TxDOT 1547 Over-Axle Weight Tolerance Permits

The trucking industry has played a significant role in the economic growth in Texas by transporting and distributing commodities using commercial motor vehicles. The Texas Department of Transportation (TxDOT), however, has recognized that the large number of overweight trucks operating on the state highway system has resulted in the deterioration of pavement condition. In addition, the permit fee to carry higher loads above legal limits is much lower than the cost to treat the increase in pavement damage. The primary purpose of the research presented in this paper is to investigate current TxDOT overweight permit structures to support pavement management. The research team analyzed the TxDOT “1547” Over-axle Weight Tolerance Permit structure to support an increase in the fee structure, bringing it more in line with the actual pavement damage. The analysis showed that the revised overweight permit structure could provide an additional $9.3 million annually for pavement maintenance needs by increasing current permit fees. These results were supported by the 2030 Committee for recommendation to the Texas Transportation Commission and consideration by the State Legislature [1]. The research team recommends conducting further research to identify methods for working cooperatively with the trucking industry to develop improved methods for assessing weight damage relationships and developing more effective and accurate means for assessing overweight permit fees.

The magic of numbers and the amazing disappearing act : the politics of counting youth homelessness

No-one wants to see young people who are no longer able to stay at home with their parents living in situations that are neither stable nor safe. Most Australians also appreciate that youth homelessness is typically a result of factors beyond the control of young people like poverty, lack of affordable housing, parental divorce or separation, family conflict and violence, sexual abuse, or mental health problems.1 Since the Burdekin Report of 1989 first put the issue on the national agenda, youth homelessness has been a point of some political sensitivity as the numbers of young homeless stayed stubbornly high through the 1990s and into the 2000s.