541 resultados para multi-framing camera


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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.

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The multi-level current reinjection concept described in literature is well-known to produce high quality AC current waveforms in high power and high voltage self-commutating current source converters. This paper proposes a novel reinjection circuitry which is capable of producing a 7-level reinjection current. It is shown that this reinjection current effectively increases the pulse number of the converter to 72. The use of PSCAD/EMTDC simulation validates the functionality of the proposed concept illustrating its effectiveness on both AC and DC sides of the converter.

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In this paper, cognitive load analysis via acoustic- and CAN-Bus-based driver performance metrics is employed to assess two different commercial speech dialog systems (SDS) during in-vehicle use. Several metrics are proposed to measure increases in stress, distraction and cognitive load and we compare these measures with statistical analysis of the speech recognition component of each SDS. It is found that care must be taken when designing an SDS as it may increase cognitive load which can be observed through increased speech response delay (SRD), changes in speech production due to negative emotion towards the SDS, and decreased driving performance on lateral control tasks. From this study, guidelines are presented for designing systems which are to be used in vehicular environments.

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Multi-resolution modelling has become essential as modern 3D applications demand 3D objects with higher LODs (LOD). Multi-modal devices such as PDAs and UMPCs do not have sufficient resources to handle the original 3D objects. The increased usage of collaborative applications has created many challenges for remote manipulation working with 3D objects of different quality. This paper studies how we can improve multi-resolution techniques by performing multiedge decimation and using annotative commands. It also investigates how devices with poorer quality 3D object can participate in collaborative actions.

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Cooperative collision warning system for road vehicles, enabled by recent advances in positioning systems and wireless communication technologies, can potentially reduce traffic accident significantly. To improve the system, we propose a graph model to represent interactions between multiple road vehicles in a specific region and at a specific time. Given a list of vehicles in vicinity, we can generate the interaction graph using several rules that consider vehicle's properties such as position, speed, heading, etc. Safety applications can use the model to improve emergency warning accuracy and optimize wireless channel usage. The model allows us to develop some congestion control strategies for an efficient multi-hop broadcast protocol.

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Background Diagnosis and treatment of cancer can contribute to psychological distress and anxiety amongst patients. Evidence indicates that information giving can be beneficial in reducing patient anxiety, so oncology specific information may have a major impact on this patient group. This study investigates the effects of an orientation program on levels of anxiety and self-efficacy amongst newly registered cancer patients who are about to undergo chemotherapy and/or radiation therapy in the cancer care centre of a large tertiary Australian hospital. Methods The concept of interventions for orienting new cancer patients needs revisiting due to the dynamic health care system. Historically, most orientation programs at this cancer centre were conducted by one nurse. A randomised controlled trial has been designed to test the effectiveness of an orientation program with bundled interventions; a face-to-face program which includes introduction to the hospital facilities, introduction to the multi-disciplinary team and an overview of treatment side effects and self care strategies. The aim is to orientate patients to the cancer centre and to meet the health care team. We hypothesize that patients who receive this orientation will experience lower levels of anxiety and distress, and a higher level of self-efficacy. Discussion An orientation program is a common health care service provided by cancer care centres for new cancer patients. Such programs aim to give information to patients at the beginning of their encounter at a cancer care centre. It is clear in the literature that interventions that aim to improve self-efficacy in patients may demonstrate potential improvement in health outcomes. Yet, evidence on the effects of orientation programs for cancer patients on self-efficacy remains scarce, particularly with respect to the use of multidisciplinary team members. This paper presents the design of a randomised controlled trial that will evaluate the effects and feasibility of a multidisciplinary orientation program for new cancer patients.

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The identification of attractors is one of the key tasks in studies of neurobiological coordination from a dynamical systems perspective, with a considerable body of literature resulting from this task. However, with regards to typical movement models investigated, the overwhelming majority of actions studied previously belong to the class of continuous, rhythmical movements. In contrast, very few studies have investigated coordination of discrete movements, particularly multi-articular discrete movements. In the present study, we investigated phase transition behavior in a basketball throwing task where participants were instructed to shoot at the basket from different distances. Adopting the ubiquitous scaling paradigm, throwing distance was manipulated as a candidate control parameter. Using a cluster analysis approach, clear phase transitions between different movement patterns were observed in performance of only two of eight participants. The remaining participants used a single movement pattern and varied it according to throwing distance, thereby exhibiting hysteresis effects. Results suggested that, in movement models involving many biomechanical degrees of freedom in degenerate systems, greater movement variation across individuals is available for exploitation. This observation stands in contrast to movement variation typically observed in studies using more constrained bi-manual movement models. This degenerate system behavior provides new insights and poses fresh challenges to the dynamical systems theoretical approach, requiring further research beyond conventional movement models.

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One of the ways in which university departments and faculties can enhance the quality of learning and assessment is to develop a ‘well thought out criterion‐referenced assessment system’ (Biggs, 2003, p. 271). In designing undergraduate degrees (courses) this entails making decisions about the levelling of expectations across different years through devising objectives and their corresponding criteria and standards: a process of alignment analogous to what happens in unit (subject) design. These decisions about levelling have important repercussions in terms of supporting students’ work‐related learning, especially in relation to their ability to cope with the increasing cognitive and skill demands made on them as they progress through their studies. They also affect the accountability of teacher judgments of students’ responses to assessment tasks, achievement of unit objectives and, ultimately, whether students are awarded their degrees and are sufficiently prepared for the world of work. Research reveals that this decision‐making process is rarely underpinned by an explicit educational rationale (Morgan et al, 2002). The decision to implement criterion referenced assessment in an undergraduate microbiology degree was the impetus for developing such a rationale because of the implications for alignment, and therefore ‘levelling’ of expectations across different years of the degree. This paper provides supporting evidence for a multi‐pronged approach to levelling, through backward mapping of two revised units (foundation and exit year). This approach adheres to the principles of alignment while combining a work‐related approach (via industry input) with the blended disciplinary and learner‐centred approaches proposed by Morgan et al. (2002). It is suggested that this multi‐pronged approach has the potential for making expectations, especially work‐related ones across different year levels of degrees, more explicit to students and future employers.

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The proliferation of innovative schemes to address climate change at international, national and local levels signals a fundamental shift in the priority and role of the natural environment to society, organizations and individuals. This shift in shared priorities invites academics and practitioners to consider the role of institutions in shaping and constraining responses to climate change at multiple levels of organisations and society. Institutional theory provides an approach to conceptualising and addressing climate change challenges by focusing on the central logics that guide society, organizations and individuals and their material and symbolic relationship to the environment. For example, framing a response to climate change in the form of an emission trading scheme evidences a practice informed by a capitalist market logic (Friedland and Alford 1991). However, not all responses need necessarily align with a market logic. Indeed, Thornton (2004) identifies six broad societal sectors each with its own logic (markets, corporations, professions, states, families, religions). Hence, understanding the logics that underpin successful –and unsuccessful– climate change initiatives contributes to revealing how institutions shape and constrain practices, and provides valuable insights for policy makers and organizations. This paper develops models and propositions to consider the construction of, and challenges to, climate change initiatives based on institutional logics (Thornton and Ocasio 2008). We propose that the challenge of understanding and explaining how climate change initiatives are successfully adopted be examined in terms of their institutional logics, and how these logics evolve over time. To achieve this, a multi-level framework of analysis that encompasses society, organizations and individuals is necessary (Friedland and Alford 1991). However, to date most extant studies of institutional logics have tended to emphasize one level over the others (Thornton and Ocasio 2008: 104). In addition, existing studies related to climate change initiatives have largely been descriptive (e.g. Braun 2008) or prescriptive (e.g. Boiral 2006) in terms of the suitability of particular practices. This paper contributes to the literature on logics by examining multiple levels: the proliferation of the climate change agenda provides a site in which to study how institutional logics are played out across multiple, yet embedded levels within society through institutional forums in which change takes place. Secondly, the paper specifically examines how institutional logics provide society with organising principles –material practices and symbolic constructions– which enable and constrain their actions and help define their motives and identity. Based on this model, we develop a series of propositions of the conditions required for the successful introduction of climate change initiatives. The paper proceeds as follows. We present a review of literature related to institutional logics and develop a generic model of the process of the operation of institutional logics. We then consider how this is applied to key initiatives related to climate change. Finally, we develop a series of propositions which might guide insights into the successful implementation of climate change practices.

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Classical negotiation models are weak in supporting real-world business negotiations because these models often assume that the preference information of each negotiator is made public. Although parametric learning methods have been proposed for acquiring the preference information of negotiation opponents, these methods suffer from the strong assumptions about the specific utility function and negotiation mechanism employed by the opponents. Consequently, it is difficult to apply these learning methods to the heterogeneous negotiation agents participating in e‑marketplaces. This paper illustrates the design, development, and evaluation of a nonparametric negotiation knowledge discovery method which is underpinned by the well-known Bayesian learning paradigm. According to our empirical testing, the novel knowledge discovery method can speed up the negotiation processes while maintaining negotiation effectiveness. To the best of our knowledge, this is the first nonparametric negotiation knowledge discovery method developed and evaluated in the context of multi-issue bargaining over e‑marketplaces.

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This article describes the development and validation of a multi-dimensional scale for measuring managers’ perceptions of the range of factors that routinely guide their decision-making processes. An instrument for identifying managerial ethical profiles (MEP) is developed by measuring the perceived role of different ethical principles in the decision-making of managers. Evidence as to the validity of the multidimensionality of the ethical scale is provided, based on the comparative assessment of different models for managerial ethical decision-making. Confirmatory Factor Analysis (CFA) supported a eight-factor model including two factors for each of the main four schools of moral philosophy. Future research needs and the value of this measure to business ethics are discussed.

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This paper reports the application of multicriteria decision making techniques, PROMETHEE and GAIA, and receptor models, PCA/APCS and PMF, to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. While PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulphate, sea salt and road dust/soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

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How is contemporary culture 'framed' - understood, promoted, dissected and defended - in the new approaches being employed in university education today? How do these approaches compare with those seen in the public policy process? What are the implications of these differences for future directions in theory, education, activism and policy? Framing Culture looks at cultural and media studies, which are rapidly growing fields through which students are introduced to contemporary cultural industries such as television, film and video. It compares these approaches with those used to frame public policy and finds a striking lack of correspondence between them. Issues such as Australian content on commercial television and in advertising, new technologies and new media, and violence in the media all highlight the gap between contemporary cultural theories and the way culture and communications are debated in public policy. The reasons for this gap must be investigated before closer relations can be established. Framing Culture brings together cultural studies and policy studies in a lively and innovative way. It suggests avenues for cultural activism that have been neglected in cultural theory and practice, and it will provoke debates which are long overdue.

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Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

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Tzeng et al. proposed a new threshold multi-proxy multi-signature scheme with threshold verification. In their scheme, a subset of original signers authenticates a designated proxy group to sign on behalf of the original group. A message m has to be signed by a subset of proxy signers who can represent the proxy group. Then, the proxy signature is sent to the verifier group. A subset of verifiers in the verifier group can also represent the group to authenticate the proxy signature. Subsequently, there are two improved schemes to eliminate the security leak of Tzeng et al.’s scheme. In this paper, we have pointed out the security leakage of the three schemes and further proposed a novel threshold multi-proxy multi-signature scheme with threshold verification.