Group segmentation during object tracking using optical flow discontinuities

Within a surveillance video, occlusions are commonplace, and accurately resolving these occlusions is key when seeking to accurately track objects. The challenge of accurately segmenting objects is further complicated by the fact that within many real-world surveillance environments, the objects appear very similar. For example, footage of pedestrians in a city environment will consist of many people wearing dark suits. In this paper, we propose a novel technique to segment groups and resolve occlusions using optical flow discontinuities. We demonstrate that the ratio of continuous to discontinuous pixels within a region can be used to locate the overlapping edges, and incorporate this into an object tracking framework. Results on a portion of the ETISEO database show that the proposed algorithm results in improved tracking performance overall, and improved tracking within occlusions.

Security metrics for object-oriented designs

Several studies have developed metrics for software quality attributes of object-oriented designs such as reusability and functionality. However, metrics which measure the quality attribute of information security have received little attention. Moreover, existing security metrics measure either the system from a high level (i.e. the whole system’s level) or from a low level (i.e. the program code’s level). These approaches make it hard and expensive to discover and fix vulnerabilities caused by software design errors. In this work, we focus on the design of an object-oriented application and define a number of information security metrics derivable from a program’s design artifacts. These metrics allow software designers to discover and fix security vulnerabilities at an early stage, and help compare the potential security of various alternative designs. In particular, we present security metrics based on composition, coupling, extensibility, inheritance, and the design size of a given object-oriented, multi-class program from the point of view of potential information flow.

Assessing the impact of refactoring on security-critical object-oriented designs

Refactoring focuses on improving the reusability, maintainability and performance of programs. However, the impact of refactoring on the security of a given program has received little attention. In this work, we focus on the design of object-oriented applications and use metrics to assess the impact of a number of standard refactoring rules on their security by evaluating the metrics before and after refactoring. This assessment tells us which refactoring steps can increase the security level of a given program from the point of view of potential information flow, allowing application designers to improve their system’s security at an early stage.

The impact of managers' characteristics on the use of public relations strategy : an exploratory study in Queensland schools

While previous positive and normative studies have focused on the role public relations should play in organisations and the need for management in all organisations to attend to public relations (Cutlip et al., 2006), there has been little discussion in the public relations literature on why or how managers choose to enact public relations strategies for their organisations. If the discipline of public relations is to cement itself as a management function, then researchers must gain a better understanding of managers themselves given that they are the ones who decide if and how public relations strategies should be employed in the organisation. This study has sought to explore evidence of a relationship between management characteristics and their impact on decisions managers make when choosing which public relations strategies to adopt in response to changes in the organisation’s operating environment. This exploratory research study has been conducted within a specific context of schools in Queensland, Australia. Queensland schools have been facing a number of changes within their operating environment due to changes in Federal funding models in Australia’s education system. This study used an exploratory, qualitative approach to understand the management characteristics demonstrated by managers in schools and how these have impacted on the selection of public relations strategies for responding to their changing and increasingly competitive environment. The unit of analysis for this research study was principals in State (government) schools and in non-government schools. Ten principals were interviewed from four different types of schools in Queensland – the more traditional, elite, private schools (GPS Schools); other Independent Schools; Catholic Schools; and State (or public) schools. These interviews were analysed for quantitative comparisons of the managers’ characteristics across the different schools (in terms of the number of principals in each age bracket, those holding postgraduate qualifications, years of experience etc.); and for qualitative data to provide a greater sense of their understanding of public relations. The 10 schools were selected within a geographic area from Brisbane’s inner city to its outer western suburbs to include an element of competition amongst those managers being interviewed. A detailed review of government, school and other public documents was also conducted to gain an insight into the environment in which principals made decisions about public relations strategy to respond to increasing competition. This study found support for the literature on the relationship between management characteristics and strategy. However, there was also variation in findings warranting further investigation of the literature on the relationship between management characteristics and strategy in a school setting. Key relationships found in this study were between: management characteristics themselves; age and the use of public relations strategies; and gender and the use of public relations strategies. There was also evidence of support for the literature linking the impact that the combination of managers’ age, education and experience had on the use of public relations strategies. While this study was exploratory in nature, it did reveal a number of areas that require further investigation to gain a deeper understanding of how and why managers choose public relations strategies as a response to changes in their operating environment. It also provided a different framework to gain a better understanding of managers’ understanding and support of public relations in schools, which, in conjunction with an analysis of their management characteristics, will hopefully allow public relations scholars and practitioners alike gain an understanding of how and why managers use public relations strategies.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

A distributed protocol for object tracking in wireless multimedia sensor networks

We consider the problem of object tracking in a wireless multimedia sensor network (we mainly focus on the camera component in this work). The vast majority of current object tracking techniques, either centralised or distributed, assume unlimited energy, meaning these techniques don't translate well when applied within the constraints of low-power distributed systems. In this paper we develop and analyse a highly-scalable, distributed strategy to object tracking in wireless camera networks with limited resources. In the proposed system, cameras transmit descriptions of objects to a subset of neighbours, determined using a predictive forwarding strategy. The received descriptions are then matched at the next camera on the objects path using a probability maximisation process with locally generated descriptions. We show, via simulation, that our predictive forwarding and probabilistic matching strategy can significantly reduce the number of object-misses, ID-switches and ID-losses; it can also reduce the number of required transmissions over a simple broadcast scenario by up to 67%. We show that our system performs well under realistic assumptions about matching objects appearance using colour.