999 resultados para home detention


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Background
Time spent watching television affects multiple aspects of child and adolescent health. Although a diverse range of factors have been found to be associated with young people's television viewing, parents and the home environment are particularly influential. However, little is known about whether parents, particularly those who are concerned about their child's television viewing habits, translate their concern into action by providing supportive home environments (e.g. rules restricting screen-time behaviours, limited access to screen-based media). The aim of this study was to examine associations between parental concerns for child television viewing and child television viewing and the home sedentary environment.
Methods
Parents of children aged 5-6 years ('younger' children, n = 430) and 10-12 years ('older children', n = 640) reported usual duration of their child's television (TV) viewing, their concerns regarding the amount of time their child spends watching TV, and on aspects of the home environment. Regression analyses examined associations between parental concern and child TV viewing, and between parental concern and aspects of the home environment. Analyses were stratified by age group.
Results
Children of concerned parents watched more TV than those whose parents were not concerned (B = 9.63, 95% CI = 1.58-17.68, p = 0.02 and B = 15.82, 95% CI = 8.85-22.80, p < 0.01, for younger and older children respectively). Parental concern was positively associated with younger children eating dinner in front of the television, and with parental restriction of sedentary behaviours and offering sedentary activities (i.e. TV viewing or computer use) as a reward for good behaviour among older and young children. Furthermore, parents of older children who were concerned had fewer televisions in the home and a lower count of sedentary equipment in the home.
Conclusions
Children of concerned parents watched more TV than those whose parents who were not concerned. Parents appear to recognise excessive television viewing in their children and these parents appear to engage in conflicting parental approaches despite these concerns. Interventions targeting concerned parents may be an innovative way of reaching children most in need of strategies to reduce their television viewing and harnessing this parental concern may offer considerable opportunity to change the family and home environment.

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This paper describes a novel suggestive interface embedded in a smart camera prototype aimed at aiding home movie makers. We focus on the problem of generating shot capture suggestions suitable to the user's filming context, intended audience and style, and formulate a novel aesthetic measure by which to judge proposed suggestions. Tight coupling between media and software allows the aesthetic measure to be sensitive to previous footage captures, including those taken without the system's prompting, in a manner allowing flexible, end-to-end migration of the authoring task from user to machine. An approximate method is used to find timely, near-optimal solutions to the aesthetic measure. Qualitative evaluation in the form of a user study shows it to be a promising approach to the flexible home movie authoring context.

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This paper describes an approach to representing normal activities in a smart house based on the concept of anxiety. Anxiety is computed as a function of time and is kept low by interactions of an occupant with the various devices in a house. Abnormality is indicated by a lack of activity or the wrong activity which will cause anxiety to rise ultimately raising an alarm, querying the occupant and/or alerting a carer in real-time. Anxiety is formulated using probabilistic models that describe how people interact with devices in combinations. These models can be learnt interactively as the smart house acts pessimistically enquiring of the occupant if what they are doing is normal. Results are presented for a number of kitchen scenarios and for different formulations of anxiety.

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Spatial activity recognition is challenging due to the amount of noise incorporated during video tracking in everyday environments. We address the spatial recognition problem with a biologically-inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to change motile behaviour in relation to environmental gradients. Through adoption of chemotactic principles, we propose the chemotactic model and evaluate its performance in a smart house environment. The model exhibits greater than 99% recognition performance with a diverse six class dataset and outperforms the Hidden Markov Model (HMM). The approach also maintains high accuracy (90-99%) with small training sets of one to five sequences. Importantly, unlike other low-level spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities.

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This paper details the design of an algorithm for automatically manipulating the important aesthetic element of video, visual tempo. Automatic injection, detection and repair of such aesthetic elements, it is argued, is vital to the next generation of amateur multimedia authoring tools. We evaluate the performance of the algorithm on a battery of synthetic data and demonstrate its ability to return the visual tempo of the final media a considerable degree closer to the target signal. The novelty of this work lies chiefly in the systematic manipulation of this high level aesthetic element of video.

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We present a new aspect of our ongoing research aimed at providing technology for the amateur home videographer. We aim to enable the production of quality video presentations that are well structured and use the expressive properties of the medium to full effect, regardless of the technical or artistic abilities of the user. This task requires that help be given to the user at or before capture time. We use a PDA platform to deliver 3d visualizations of shot directives, instructions to the user about the type of footage to capture, and discuss issues connected with realizing high-level representations in concrete first person animations. Additionally, we discuss the mechanism for mating that metadata with captured footage and implementation issues.

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In this paper we present an application designed to improve the quality of amateur video production. The majority of home movie material is negatively impacted by two factors: lack of narrative content- "what to shoot?", and the absence or inappropriate use of cinesthetic elements for effective reinforcement of content - "how to shoot?". We leverage the age-old communicative powers of Story to answer the what For the second problem, the how, we turn to the corpus of aesthetic principles that constitute the film profession, which impact both technical and cinematic considerations for a given project.

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In this paper we describe research aimed at enabling amateur video makers to improve both the technical quality and communicative capacity of their work. Motivated by the recognition that untold hours of home video are simply abandoned after capture, we have formulated the problem as one of defining the what and how of footage capture. We have implemented a framework that answers the first problem, the what, by means of the age-old communicative powers of Story; the second problem, the how, is addressed by means of well documented aesthetic principles that constitute the film profession, which impact both technical and cinematic considerations for a given project. We provide a brief overview of the process, beginning with the narrative template, embodying a chosen story, through the principal phases of generating a storyboard, directing, and editing, resulting in the finished product. We demonstrate the interplay of narrative, purpose for the production, and aesthetic agents, and their influence on the automatically generated storyboard with examples.

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The identification of useful structures in home video is difficult because this class of video is distinguished from other video sources by its unrestricted, non edited content and the absence of regulated storyline. In addition, home videos contain a lot of motion and erratic camera movements, with shots of the same character being captured from various angles and viewpoints. In this paper, we present a solution to the challenging problem of clustering shots and faces in home videos, based on the use of SIFT features. SIFT features have been known to be robust for object recognition; however, in dealing with the complexities of home video setting, the matching process needs to be augmented and adapted. This paper describes various techniques that can improve the number of matches returned as well as the correctness of matches. For example, existing methods for verification of matches are inadequate for cases when a small number of matches are returned, a common situation in home videos. We address this by constructing a robust classifier that works on matching sets instead of individual matches, allowing the exploitation of the geometric constraints between matches. Finally, we propose techniques for robustly extracting target clusters from individual feature matches.

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This paper describes a novel interactive media authoring framework, MediaTE, that enables amateurs to create videos of higher narrative or aesthetic quality with a completely mobile lifecycle. A novel event bootstrapping dialog is used to derive shot suggestions that yield both targetted footage and annotation enabling an automatic Computational Media Aesthetics-aware editing phase, the manual performance of which is typically a barrier to the amateur. This facilitates a move away from requiring a prior-conception of the events or locale being filmed, in the form of a template, to at-capture bootstrapping of this information. Metadata gathered as part of the critical path of media creation also has implications for the longevity and reuse of captured media assets. Results of an evaluation performed on both the usability and delivered media aspects of the system are discussed, which highlight the tenability of the proposed framework and the quality of the produced media.

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The aim of this work is to devise an effective method for static summarization of home video sequences. Based on the premise that the user watching a summary is interested in people related (how many, who, emotional state) or activity related aspects, we formulate a novel approach to video summarization that works to specifically expose relevant video frames that make the content spotting tasks possible. Unlike existing approaches, which work on low-level features which often produce the summary not appealing to the viewer due to the semantic gap between low-level features and high-level concepts, our approach is driven by various utility functions (identity count, identity recognition, emotion recognition, activity recognition, sense of space) that use the results of face detection, face clustering, shot clustering and within cluster frame alignment. The summarization problem is then treated as the problem of extracting the set of key frames that have the maximum combined utility.

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This paper presents novel additions to our existing amateur media creation framework. The framework provides at-capture guidance to enable the home movie maker to realize their aesthetic and narrative goals and automation of post-production editing. A common problem with the amateur filming context is its contingent nature, which often results in the failure to gain footage vital to the user's goals, even with at-capture software embedding. Accordingly, we have modelled minimizing the difference between target and captured footage at a given time during filming as a probability distribution divergence problem. We apply two policies of feedback to the user on their performance, passive communication via a suggestion desirability measure, and active filtering of undesirable suggestions. We demonstrate the framework using each policy with a simulation of various user and filming situations with promising results.